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E. Abbasnejad, S. Sanner, E. V. Bonilla and P. Poupart. Learning Community-based Preferences via Dirichlet Process Mixtures of Gaussian Processes. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), 2013.
BibTeX:
@inproceedings{Abbasnejad2013,
  author = {Abbasnejad, E. and Sanner, S. and Bonilla, E. V. and Poupart, P.},
  title = {Learning Community-based Preferences via {Dirichlet} Process Mixtures of {Gaussian} Processes},
  booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13)},
  year = {2013}
}
Fabio Aiolli. A Preference Model for Structured Supervised Learning Tasks. In Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05), pages 557-560, IEEE Computer Society, 2005.
BibTeX:
@incollection{GPLM,
  author = {Aiolli, Fabio},
  title = {A Preference Model for Structured Supervised Learning Tasks},
  booktitle = {Proceedings of the Fifth IEEE International Conference on Data Mining (ICDM-05)},
  publisher = {IEEE Computer Society},
  year = {2005},
  pages = {557--560}
}
Fabio Aiolli and Alessandro Sperduti. A Preference Optimization based Unifying Framework for Supervised Learning Problems. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 19-42, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Aiolli,
  author = {Aiolli, Fabio and Sperduti, Alessandro},
  title = {A Preference Optimization based Unifying Framework for Supervised Learning Problems},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {19--42},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Riad Akrour, Marc Schoenauer and Michèle Sebag. APRIL: Active Preference Learning-Based Reinforcement Learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD-12), Vol. 7524, pages 116-131, Springer, 2012.
BibTeX:
@inproceedings{aprilsebag,
  author = {Akrour, Riad and Schoenauer, Marc and Sebag, Mich{\`{e}}le},
  title = {{APRIL}: Active Preference Learning-Based Reinforcement Learning},
  booktitle = {Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD-12)},
  publisher = {Springer},
  year = {2012},
  volume = {7524},
  pages = {116-131},
  doi = {http://dx.doi.org/10.1007/978-3-642-33486-3\_8}
}
Erin L. Allwein, Robert E. Schapire and Yoram Singer. Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research, Cambridge, MA, USA, Vol. 1, pages 113-141, MIT Press, 2000.
BibTeX:
@article{ReducingMultiClass,
  author = {Allwein, Erin L. and Schapire, Robert E. and Singer, Yoram},
  title = {Reducing multiclass to binary: a unifying approach for margin classifiers},
  journal = {Journal of Machine Learning Research},
  publisher = {MIT Press},
  year = {2000},
  volume = {1},
  pages = {113--141}
}
Erin L. Allwein, Robert E. Schapire and Yoram Singer. Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. In Proceedings of the 17th International Conference on Machine Learning (ICML-2000), pages 9-16, Morgan Kaufmann, 2000.
BibTeX:
@inproceedings{ReducingMultiClass-1,
  author = {Allwein, Erin L. and Schapire, Robert E. and Singer, Yoram},
  title = {Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers},
  booktitle = {Proceedings of the 17th International Conference on Machine Learning (ICML-2000)},
  publisher = {Morgan Kaufmann},
  year = {2000},
  pages = {9--16}
}
C. Alonso, J. J. Rodrguez and B. Pulido. Enhancing Consistency-Based Diagnosis with Machine Learning Techniques. In Current Topics in Artificial Intelligence, pages 312-321, Springer-Verlag, 2004.
BibTeX:
@incollection{NetworkFailureExample,
  author = {Alonso, C. and Rodr{\'{\i}}guez, J. J. and Pulido, B.},
  title = {Enhancing Consistency-Based Diagnosis with Machine Learning Techniques},
  booktitle = {Current Topics in Artificial Intelligence},
  publisher = {Springer-Verlag},
  year = {2004},
  pages = {312--321}
}
Cecilio Angulo and Andreu Català. $K$-SVCR. A Multi-class Support Vector Machine. In Proceedings of the 11th European Conference on Machine Learning (ECML-2000) (Ramon López de Mántaras and E. Plaza, editors), pages 31-38, Springer-Verlag, 2000.
BibTeX:
@inproceedings{PairwiseSVM-ThreeWay-2,
  author = {Angulo, Cecilio and Catal{\`{a}}, Andreu},
  title = {{$K$-SVCR}. {A} Multi-class Support Vector Machine},
  booktitle = {Proceedings of the 11th European Conference on Machine Learning (ECML-2000)},
  editor = {L{\'{o}}pez de M{\'{a}}ntaras, Ramon and Plaza, E.},
  publisher = {Springer-Verlag},
  year = {2000},
  pages = {31--38}
}
Cecilio Angulo, Xavier Parra and Andreu Català. $K$-SVCR. A Support Vector Machine for Multi-Class Classification. Neurocomputing, Vol. 55(1-2), pages 57-77, 2003.
BibTeX:
@article{PairwiseSVM-ThreeWay,
  author = {Angulo, Cecilio and Parra, Xavier and Catal{\`{a}}, Andreu},
  title = {{$K$-SVCR}. {A} Support Vector Machine for Multi-Class Classification},
  journal = {Neurocomputing},
  year = {2003},
  volume = {55},
  number = {1--2},
  pages = {57--77}
}
Cecilio Angulo, Francisco J. Ruiz, Luis González and Juan Antonio Ortega. Multi-Classification by Using Tri-Class SVM. Neural Processing Letters, Vol. 23(1), pages 89-101, 2006.
BibTeX:
@article{TriClassSVM,
  author = {Angulo, Cecilio and Ruiz, Francisco J. and Gonz{\'{a}}lez, Luis and Ortega, Juan Antonio},
  title = {Multi-Classification by Using Tri-Class {SVM}},
  journal = {Neural Processing Letters},
  year = {2006},
  volume = {23},
  number = {1},
  pages = {89--101}
}
Robert Arens. Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 363-383, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Arens,
  author = {Arens, Robert},
  title = {Learning {SVM} Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {363--383},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Bing Bai, Jason Weston, David Grangier, Ronan Collobert, Kunihiko Sadamasa, Yanjun Qi, Olivier Chapelle and Kilian Q. Weinberger. Learning to rank with (a lot of) word features. Inf. Retr., Vol. 13(3), pages 291-314, 2010.
BibTeX:
@article{DBLP:journals/ir/BaiWGCSQCW10,
  author = {Bai, Bing and Weston, Jason and Grangier, David and Collobert, Ronan and Sadamasa, Kunihiko and Qi, Yanjun and Chapelle, Olivier and Weinberger, Kilian Q.},
  title = {Learning to rank with (a lot of) word features},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {291-314},
  doi = {http://dx.doi.org/10.1007/s10791-009-9117-9}
}
Rajarajeswari Balasubramaniyan, Eyke Hüllermeier, Nils Weskamp and Jörg Kämper. Clustering of Gene Expression Data Using a Local Shape-Based Similarity Measure. Bioinformatics, Vol. 21(7), pages 1069-1077, 2005.
BibTeX:
@article{bhwk05,
  author = {Balasubramaniyan, Rajarajeswari and H{\"{u}}llermeier, Eyke and Weskamp, Nils and K{\"{a}}mper, J{\"{o}}rg},
  title = {Clustering of Gene Expression Data Using a Local Shape-Based Similarity Measure},
  journal = {Bioinformatics},
  year = {2005},
  volume = {21},
  number = {7},
  pages = {1069--1077}
}
J. J. Bartholdi, C. A. Tovey and M. A. Trick. Voting schemes for which it can be difficult to tell who won the election. Social Choice and Welfare, Vol. 6(2), pages 157-165, 1989.
BibTeX:
@article{VotingSchemes,
  author = {Bartholdi, J. J. and Tovey, C. A. and Trick, M. A.},
  title = {Voting schemes for which it can be difficult to tell who won the election},
  journal = {Social Choice and Welfare},
  year = {1989},
  volume = {6},
  number = {2},
  pages = {157--165}
}
Alejandro Bellogn, Iván Cantador, Pablo Castells and Álvaro Ortigosa. Discerning Relevant Model Features in a Content-based Collaborative Recommender System. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 429-455, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Bellogin,
  author = {Bellog{\'{\i}}n, Alejandro and Cantador, Iv{\'{a}}n and Castells, Pablo and Ortigosa, {\'{A}}lvaro},
  title = {Discerning Relevant Model Features in a Content-based Collaborative Recommender System},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {429--455},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Alina Beygelzimer, John Langford and Bianca Zadrozny. Machine Learning Techniques---Reductions Between Prediction Quality Metrics. In Performance Modeling and Engineering (Z. Liu and C. H. Xia, editors), pages 3-28, Springer-Verlag, 2008.
BibTeX:
@incollection{Reduction-PerformanceMetrics,
  author = {Beygelzimer, Alina and Langford, John and Zadrozny, Bianca},
  title = {Machine Learning Techniques---Reductions Between Prediction Quality Metrics},
  booktitle = {Performance Modeling and Engineering},
  editor = {Liu, Z. and Xia, C. H.},
  publisher = {Springer-Verlag},
  year = {2008},
  pages = {3--28},
  url = {http://hunch.net/~jl/projects/reductions/tutorial/paper/chapter.pdf}
}
Adriana Birlutiu and Tom Heskes. Expectation Propagation for Rating Players in Sports Competitions. In Proceedings of the 11th European Symposium on Principles of Knowledge Discovery in Databases (PKDD-07) (J. N. Kok, J. Koronacki, R. López de Mántaras, S. Matwin, Dunja Mladenić and A. Skowron, editors), Warsaw, Poland, pages 374-381, Springer-Verlag, 2007.
BibTeX:
@inproceedings{RankAggregation-Tennis,
  author = {Birlutiu, Adriana and Heskes, Tom},
  title = {Expectation Propagation for Rating Players in Sports Competitions},
  booktitle = {Proceedings of the 11th European Symposium on Principles of Knowledge Discovery in Databases (PKDD-07)},
  editor = {Kok, J. N. and Koronacki, J. and de M{\'{a}}ntaras, R. L{\'{o}}pez and Matwin, S. and Mladeni{\'{c}}, Dunja and Skowron, A.},
  publisher = {Springer-Verlag},
  year = {2007},
  pages = {374--381}
}
Edwin V. Bonilla, Shengbo Guo and Scott Sanner. Gaussian Process Preference Elicitation. In Advances in Neural Information Processing Systems 23 (NIPS-10) (John D. Lafferty, Christopher K. I. Williams, John Shawe-Taylor, Richard S. Zemel and Aron Culotta, editors), pages 262-270, Curran Associates, Inc., 2010.
BibTeX:
@inproceedings{PreferenceElicitation-Gaussian,
  author = {Bonilla, Edwin V. and Guo, Shengbo and Sanner, Scott},
  title = {Gaussian Process Preference Elicitation},
  booktitle = {Advances in Neural Information Processing Systems 23 (NIPS-10)},
  editor = {Lafferty, John D. and Williams, Christopher K. I. and Shawe-Taylor, John and Zemel, Richard S. and Culotta, Aron},
  publisher = {Curran Associates, Inc.},
  year = {2010},
  pages = {262--270}
}
Matthew R. Boutell, Jiebo Luo, Xipeng Shen and C. M. Christopher M. Brown. Learning Multi-Label Scene Classification. Pattern Recognition, Vol. 37(9), pages 1757-1771, 2004.
BibTeX:
@article{Scene-Data,
  author = {Boutell, Matthew R. and Luo, Jiebo and Shen, Xipeng and Brown, C. M. Christopher M.},
  title = {Learning Multi-Label Scene Classification},
  journal = {Pattern Recognition},
  year = {2004},
  volume = {37},
  number = {9},
  pages = {1757--1771},
  url = {http://www.rose-hulman.edu/~boutell/publications/boutell04PRmultilabel.pdf}
}
Craig Boutilier, Ronen Brafman, Carmel Domshlak, Holger Hoos and David Poole. CP-nets: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements. Journal of Artificial Intelligence Research, Vol. 21, pages 135-191, 2004.
BibTeX:
@article{CP-nets,
  author = {Boutilier, Craig and Brafman, Ronen and Domshlak, Carmel and Hoos, Holger and Poole, David},
  title = {{CP-nets}: A Tool for Representing and Reasoning with Conditional Ceteris Paribus Preference Statements},
  journal = {Journal of Artificial Intelligence Research},
  year = {2004},
  volume = {21},
  pages = {135--191}
}
Craig Boutilier, Ronen Brafman, Chris Geib and David Poole. A Constraint-Based Approach to Preference Elicitation and Decision Making. In In AAAI Spring Symposium on Qualitative Decision Theory, 1997.
BibTeX:
@inproceedings{Boutilier97aconstraint-based,
  author = {Boutilier, Craig and Brafman, Ronen and Geib, Chris and Poole, David},
  title = {A Constraint-Based Approach to Preference Elicitation and Decision Making},
  booktitle = {In AAAI Spring Symposium on Qualitative Decision Theory},
  year = {1997}
}
Denis Bouyssou. Ranking Methods Based on Valued Preference Relations: A Characterization of the Net Flow Method. European Journal of Operational Research, Vol. 60(1), pages 61-67, 1992.
BibTeX:
@article{NetFlow,
  author = {Bouyssou, Denis},
  title = {Ranking Methods Based on Valued Preference Relations: A Characterization of the Net Flow Method},
  journal = {European Journal of Operational Research},
  year = {1992},
  volume = {60},
  number = {1},
  pages = {61--67}
}
Ralph A. Bradley and Milton E. Terry. The Rank Analysis of Incomplete Block Designs --- I. The Method of Paired Comparisons. Biometrika, Vol. 39, pages 324-345, 1952.
BibTeX:
@article{BradleyTerry,
  author = {Bradley, Ralph A. and Terry, Milton E.},
  title = {The Rank Analysis of Incomplete Block Designs --- {I. The} Method of Paired Comparisons},
  journal = {Biometrika},
  year = {1952},
  volume = {39},
  pages = {324--345}
}
Ronen I. Brafman. Preferences, Planning and Control. In Proceedings of the 11th Conference on Principles of Knowledge Representation and Reasoning (KR-08) (G. Brewka and J. Lang, editors), Sydney, Australia, pages 2-5, AAAI Press, 2008.
BibTeX:
@inproceedings{Preferences-Planning,
  author = {Brafman, Ronen I.},
  title = {Preferences, Planning and Control},
  booktitle = {Proceedings of the 11th Conference on Principles of Knowledge Representation and Reasoning (KR-08)},
  editor = {Brewka, G. and Lang, J.},
  publisher = {AAAI Press},
  year = {2008},
  pages = {2--5}
}
S. Brams and P. Fishburn. Voting Procedures. In Handbook of Social Choice and Welfare (Vol. 1) (K. J. Arrow, A. K. Sen and K. Suzumura, editors), Elsevier, 2002.
BibTeX:
@incollection{VotingProcedures,
  author = {Brams, S. and Fishburn, P.},
  title = {Voting Procedures},
  booktitle = {Handbook of Social Choice and Welfare (Vol. 1)},
  editor = {Arrow, K. J. and Sen, A. K. and Suzumura, K.},
  publisher = {Elsevier},
  year = {2002}
}
Christian Brinker. Graded multilabel classification by pairwise comparison. School: TU Darmstadt, Knowledge Engineering Group. May, , 2013.
BibTeX:
@mastersthesis{ba:Brinker,
  author = {Brinker, Christian},
  title = {Graded multilabel classification by pairwise comparison},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2013},
  url = {http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2013/Brinker_Christian.pdf}
}
Christian Brinker, Eneldo Loza Menca and Johannes Fürnkranz. Graded Multilabel Classification by Pairwise Comparisons. In 2014 IEEE International Conference on Data Mining (ICDM 2014), Shenzhen, China December, , pages 731-736, Curran Associates, IEEE, 2014.
Abstract: The task in multilabel classification is to predict for a given set of labels whether each individual label should be attached to an instance or not. Graded multilabel classification generalizes this setting by allowing to specify for each label a degree of membership on an ordinal scale. This setting can be frequently found in practice, for example when movies or books are assessed on a one-to-five star rating in multiple categories. In this paper, we propose to reformulate the problem in terms of preferences between the labels and their scales, which then be tackled by learning from pairwise comparisons. We present three different approaches which make use of this decomposition and show on three datasets that we are able to outperform baseline approaches. In particular, we show that our solution, which is able to model pairwise preferences across multiple scales, outperforms a straight-forward approach which considers the problem as a set of independent ordinal regression tasks.
BibTeX:
@inproceedings{brinker14gmlc,
  author = {Brinker, Christian and Loza Menc{\'{\i}}a, Eneldo and F{\"{u}}rnkranz, Johannes},
  title = {Graded Multilabel Classification by Pairwise Comparisons},
  booktitle = {2014 IEEE International Conference on Data Mining (ICDM 2014)},
  publisher = {Curran Associates, IEEE},
  year = {2014},
  pages = {731--736},
  url = {www.ke.tu-darmstadt.de/publications/papers/ICDM14graded.pdf},
  doi = {http://dx.doi.org/10.1109/ICDM.2014.102}
}
Christian Brinker, Eneldo Loza Menca and Johannes Fürnkranz. Graded Multilabel Classification by Pairwise Comparisons. December, , 2014.
Abstract: The task in multilabel classification is to predict for a given set of labels whether each individual label should be attached to an instance or not. Graded multilabel classification generalizes this setting by allowing to specify for each label a degree of membership on an ordinal scale. This setting can be frequently found in practice, for example when movies or books are assessed on a one-to-five star rating in multiple categories. In this paper, we propose to reformulate the problem in terms of preferences between the labels and their scales, which then be tackled by learning from pairwise comparisons. We present three different approaches which make use of this decomposition and show on three datasets that we are able to outperform baseline approaches. In particular, we show that our solution, which is able to model pairwise preferences across multiple scales, outperforms a straight-forward approach which considers the problem as a set of independent ordinal regression tasks.
BibTeX:
@techreport{TUD-KE-2014-01,
  author = {Brinker, Christian and Loza Menc{\'{\i}}a, Eneldo and F{\"{u}}rnkranz, Johannes},
  title = {Graded Multilabel Classification by Pairwise Comparisons},
  year = {2014},
  note = {Longer version of publication at the ICDM 2014, http://www.ke.tu-darmstadt.de/bibtex/publications/show/2618},
  url = {http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2014-01.pdf}
}
Klaus Brinker. Active Learning of Label Ranking Functions. In Proceedings of the 21st International Conference on Machine Learning (ICML-04), Banff, Canada, pages 129-136, 2004.
BibTeX:
@inproceedings{Preferences-ActiveLearning,
  author = {Brinker, Klaus},
  title = {Active Learning of Label Ranking Functions},
  booktitle = {Proceedings of the 21st International Conference on Machine Learning (ICML-04)},
  year = {2004},
  pages = {129--136}
}
Klaus Brinker and Eyke Hüllermeier. Case-Based Label Ranking. In Proceedings of the 17th European Conference on Machine Learning (ECML-06) (Johannes Fürnkranz, Tobias Scheffer and Myra Spiliopoulou, editors), Berlin, Germany, pages 566-573, Springer-Verlag, 2006.
BibTeX:
@inproceedings{LabelRanking-CaseBased,
  author = {Brinker, Klaus and H{\"{u}}llermeier, Eyke},
  title = {Case-Based Label Ranking},
  booktitle = {Proceedings of the 17th European Conference on Machine Learning (ECML-06)},
  editor = {F{\"{u}}rnkranz, Johannes and Scheffer, Tobias and Spiliopoulou, Myra},
  publisher = {Springer-Verlag},
  year = {2006},
  pages = {566--573}
}
Klaus Brinker and Eyke Hüllermeier. Case-Based Multilabel Ranking. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07) (Manuela M. Veloso, editors), Hyderabad, India, pages 702-707, 2007.
BibTeX:
@inproceedings{MultiLabelRanking-CaseBased,
  author = {Brinker, Klaus and H{\"{u}}llermeier, Eyke},
  title = {Case-Based Multilabel Ranking},
  booktitle = {Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI-07)},
  editor = {Veloso, Manuela M.},
  year = {2007},
  pages = {702--707}
}
Christian Brückner. Vergleich verschiedener Ranking-Verfahren in Sport-Turnieren. School: Knowledge Engineering Group, TU Darmstadt., 2014.
BibTeX:
@mastersthesis{ba:Brueckner,
  author = {Br{\"{u}}ckner, Christian},
  title = {Vergleich verschiedener Ranking-Verfahren in Sport-Turnieren},
  school = {Knowledge Engineering Group, TU Darmstadt},
  year = {2014},
  note = {Bachelor's Thesis},
  url = {http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2014/Brueckner_Christian.pdf}
}
Jaime S. Cardoso and Joaquim F. Pinto da Costa. Learning to Classify Ordinal Data: The Data Replication Method. Journal of Machine Learning Research, Cambridge, MA, USA, Vol. 8, pages 1393-1429, MIT Press, 2007.
BibTeX:
@article{1314546,
  author = {Cardoso, Jaime S. and Pinto da Costa, Joaquim F.},
  title = {Learning to Classify Ordinal Data: The Data Replication Method},
  journal = {Journal of Machine Learning Research},
  publisher = {MIT Press},
  year = {2007},
  volume = {8},
  pages = {1393--1429}
}
Urszula Chajewska, Lise Getoor, Joseph Norman and Yuval Shahar. Utility Elicitation as a classification problem. In Proceedings of the 14th Conference on Uncertainty in AI (UAI-98) (G. F. Cooper and S. Moral, editors), pages 79-88, 1998.
BibTeX:
@inproceedings{chaj_ue98,
  author = {Chajewska, Urszula and Getoor, Lise and Norman, Joseph and Shahar, Yuval},
  title = {Utility Elicitation as a classification problem},
  booktitle = {Proceedings of the 14th Conference on Uncertainty in AI (UAI-98)},
  editor = {Cooper, G. F. and Moral, S.},
  year = {1998},
  pages = {79--88}
}
Urszula Chajewska, Daphne Koller and Dirk Ormoneit. Learning an Agent's Utility Function by Observing Behavior. In Proceedings of the 18th International Conference on Machine Learning (ICML-01), pages 35-42, 2001.
BibTeX:
@inproceedings{chaj_la01,
  author = {Chajewska, Urszula and Koller, Daphne and Ormoneit, Dirk},
  title = {Learning an Agent's Utility Function by Observing Behavior},
  booktitle = {Proceedings of the 18th International Conference on Machine Learning (ICML-01)},
  year = {2001},
  pages = {35--42}
}
Urszula Chajewska, Daphne Koller and Ronald Parr. Making Rational Decisions Using Adaptive Utility Elicitation. In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00), pages 363-369, 2000.
BibTeX:
@inproceedings{chaj_mr00,
  author = {Chajewska, Urszula and Koller, Daphne and Parr, Ronald},
  title = {Making Rational Decisions Using Adaptive Utility Elicitation},
  booktitle = {Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00)},
  year = {2000},
  pages = {363-369}
}
Urszula Chajewska, M. Kuppermann and Daphne Koller. Discovering the Structure of Utility Functions Based on Additive and Conditionally Additive Independence Properties Between Utility Attributes. In Proceedings of the 21st Annual Meeting of the Society for Medical Decision Making (MDM-99), 1999.
BibTeX:
@inproceedings{chaj_dt99,
  author = {Chajewska, Urszula and Kuppermann, M. and Koller, Daphne},
  title = {Discovering the Structure of Utility Functions Based on Additive and Conditionally Additive Independence Properties Between Utility Attributes},
  booktitle = {Proceedings of the 21st Annual Meeting of the Society for Medical Decision Making (MDM-99)},
  year = {1999}
}
Olivier Chapelle and S. Sathiya Keerthi. Efficient algorithms for ranking with SVMs. Information Retrieval, Vol. 13(3), pages 201-215, 2010.
BibTeX:
@article{RankSVM-EfficientAlgorithms,
  author = {Chapelle, Olivier and Keerthi, S. Sathiya},
  title = {Efficient algorithms for ranking with SVMs},
  journal = {Information Retrieval},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {201--215},
  doi = {http://dx.doi.org/10.1007/s10791-009-9109-9}
}
Olivier Chapelle and Mingrui Wu. Gradient descent optimization of smoothed information retrieval metrics. Inf. Retr., Vol. 13(3), pages 216-235, 2010.
BibTeX:
@article{DBLP:journals/ir/ChapelleW10,
  author = {Chapelle, Olivier and Wu, Mingrui},
  title = {Gradient descent optimization of smoothed information retrieval metrics},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {216-235},
  doi = {http://dx.doi.org/10.1007/s10791-009-9110-3}
}
Depin Chen, Yan Xiong, Jun Yan, Gui-Rong Xue, Gang Wang and Zheng Chen. Knowledge transfer for cross domain learning to rank. Inf. Retr., Vol. 13(3), pages 236-253, 2010.
BibTeX:
@article{DBLP:journals/ir/ChenXYXWC10,
  author = {Chen, Depin and Xiong, Yan and Yan, Jun and Xue, Gui-Rong and Wang, Gang and Chen, Zheng},
  title = {Knowledge transfer for cross domain learning to rank},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {236-253},
  doi = {http://dx.doi.org/10.1007/s10791-009-9111-2}
}
Weiwei Cheng, Johannes Fürnkranz, Eyke Hüllermeier and Sang-Hyeun Park. Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. In Proceedings of the 22nd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011, Athens, Greece), Part I (Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba and Michalis Vazirgiannis, editors), pages 312-327, Springer, 2011.
Abstract: This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a "preference-based" approach to reinforcement learning is a possible extension of the type of feedback an agent may learn from. In particular, while conventional RL methods are essentially confined to deal with numerical rewards, there are many applications in which this type of information is not naturally available, and in which only qualitative reward signals are provided instead. Therefore, building on novel methods for preference learning, our general goal is to equip the RL agent with qualitative policy models, such as ranking functions that allow for sorting its available actions from most to least promising, as well as algorithms for learning such models from qualitative feedback. Concretely, in this paper, we build on an existing method for approximate policy iteration based on roll-outs. While this approach is based on the use of classification methods for generalization and policy learning, we make use of a specific type of preference learning method called label ranking. Advantages of our preference-based policy iteration method are illustrated by means of two case studies.
BibTeX:
@inproceedings{jf:ECML-PKDD-11,
  author = {Cheng, Weiwei and F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke and Park, Sang-Hyeun},
  title = {Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning},
  booktitle = {Proceedings of the 22nd European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011, Athens, Greece), Part I},
  editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},
  publisher = {Springer},
  year = {2011},
  pages = {312--327},
  url = {http://www.ke.informatik.tu-darmstadt.de/publications/papers/ECML-PKDD-11.pdf}
}
Weiwei Cheng, Michaël Rademaker, Bernard De Baets and Eyke Hüllermeier. Predicting Partial Orders: Ranking with Abstention. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD-10), Part I (José L. Balcázar, Francesco Bonchi, Aristides Gionis and Michèle Sebag, editors), Barcelona, Spain, Vol. 6321, pages 215-230, Springer, 2010.
BibTeX:
@inproceedings{PredictingPartialOrders,
  author = {Cheng, Weiwei and Rademaker, Micha{\"{e}}l and Baets, Bernard De and H{\"{u}}llermeier, Eyke},
  title = {Predicting Partial Orders: Ranking with Abstention},
  booktitle = {Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD-10), Part I},
  editor = {Balc{\'{a}}zar, Jos{\'{e}} L. and Bonchi, Francesco and Gionis, Aristides and Sebag, Mich{\`{e}}le},
  publisher = {Springer},
  year = {2010},
  volume = {6321},
  pages = {215--230},
  doi = {http://dx.doi.org/10.1007/978-3-642-15880-3_20}
}
Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Megine and Bruno Zanuttini. Learning Ordinal Preference on Multiattribute Domains: the Case of CP-nets. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 273-296, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Chevaleyre,
  author = {Chevaleyre, Yann and Koriche, Fr{\'{e}}d{\'{e}}ric and Lang, J{\'{e}}r{\^{o}}me and Megine, J{\'{e}}r{\^{o}}me and Zanuttini, Bruno},
  title = {Learning Ordinal Preference on Multiattribute Domains: the Case of CP-nets},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {273--296},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
William W. Cohen, Robert E. Schapire and Yoram Singer. Learning to Order Things. Journal of Artificial Intelligence Research, Vol. 10, pages 243-270, 1999.
BibTeX:
@article{OrderThings,
  author = {Cohen, William W. and Schapire, Robert E. and Singer, Yoram},
  title = {Learning to Order Things},
  journal = {Journal of Artificial Intelligence Research},
  year = {1999},
  volume = {10},
  pages = {243--270}
}
Vincent Conitzer and Tuomas Sandholm. Vote elicitation: complexity and strategy-proofness. In Eighteenth national conference on Artificial intelligence, Menlo Park, CA, USA, pages 392-397, American Association for Artificial Intelligence, 2002.
BibTeX:
@inproceedings{777155,
  author = {Conitzer, Vincent and Sandholm, Tuomas},
  title = {Vote elicitation: complexity and strategy-proofness},
  booktitle = {Eighteenth national conference on Artificial intelligence},
  publisher = {American Association for Artificial Intelligence},
  year = {2002},
  pages = {392--397}
}
James B. Couch. Disease Management: An Overview. In The Health Care Professional's Guide to Disease Management: Patient-Centered Care for the 21st Century, Aspen Publishers, 1998.
BibTeX:
@incollection{couc_dm98,
  author = {Couch, James B.},
  title = {Disease Management: An Overview},
  booktitle = {The Health Care Professional's Guide to Disease Management: Patient-Centered Care for the 21st Century},
  publisher = {Aspen Publishers},
  year = {1998}
}
Koby Crammer and Yoram Singer. A Family of Additive Online Algorithms for Category Ranking. Journal of Machine Learning Research, Vol. 3(6), pages 1025-1058, 2003.
BibTeX:
@article{OnlineCategoryRanking,
  author = {Crammer, Koby and Singer, Yoram},
  title = {A Family of Additive Online Algorithms for Category Ranking},
  journal = {Journal of Machine Learning Research},
  year = {2003},
  volume = {3},
  number = {6},
  pages = {1025--1058},
  url = {http://www.jmlr.org/papers/v3/crammer03b.html}
}
Florin Cutzu. Polychotomous Classification with Pairwise Classifiers: A New Voting Principle. In Proceedings of the 4th International Workshop on Multiple Classifier Systems, pages 115-124, Springer, Berlin, 2003.
BibTeX:
@inproceedings{VotingAgainst-2,
  author = {Cutzu, Florin},
  title = {Polychotomous Classification with Pairwise Classifiers: A New Voting Principle},
  booktitle = {Proceedings of the 4th International Workshop on Multiple Classifier Systems},
  publisher = {Springer, Berlin},
  year = {2003},
  pages = {115--124},
  url = {ftp://ftp.cs.indiana.edu/pub/techreports/TR573.pdf}
}
Ofer Dekel, Christopher D. Manning and Yoram Singer. Log-Linear Models for Label Ranking. In Advances in Neural Information Processing Systems (NIPS-03) (S. Thrun, L. K. Saul and Bernhard Schölkopf, editors), Cambridge, MA, pages 497-504, MIT Press, 2004.
BibTeX:
@inproceedings{LogLinearLabelRanking,
  author = {Dekel, Ofer and Manning, Christopher D. and Singer, Yoram},
  title = {Log-Linear Models for Label Ranking},
  booktitle = {Advances in Neural Information Processing Systems (NIPS-03)},
  editor = {Thrun, S. and Saul, L. K. and Sch{\"{o}}lkopf, Bernhard},
  publisher = {MIT Press},
  year = {2004},
  pages = {497--504}
}
Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński and Marcin Szelag. Learning of Rule Ensembles for Multiple Attribute Ranking Problems. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 217-247, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Dembczynski,
  author = {Dembczy\'nski, Krzysztof and Kot{\l}owski, Wojciech and S{\l}owiński, Roman and Szelag, Marcin},
  title = {Learning of Rule Ensembles for Multiple Attribute Ranking Problems},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {217--247},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Sotiris Diplaris, Grigorios Tsoumakas, Pericles A. Mitkas and Ioannis P. Vlahavas. Protein Classification with Multiple Algorithms. In Proceedings of the 10th Panhellenic Conference in Informatics (PCI-05) (Panayiotis Bozanis and Elias N. Houstis, editors), Volos, Greece, pages 448-456, Springer-Verlag, 2005.
BibTeX:
@inproceedings{Genbase-Data,
  author = {Diplaris, Sotiris and Tsoumakas, Grigorios and Mitkas, Pericles A. and Vlahavas, Ioannis P.},
  title = {Protein Classification with Multiple Algorithms},
  booktitle = {Proceedings of the 10th Panhellenic Conference in Informatics (PCI-05)},
  editor = {Bozanis, Panayiotis and Houstis, Elias N.},
  publisher = {Springer-Verlag},
  year = {2005},
  pages = {448--456}
}
Denny Dittmar. Ranking von Schach-Evaluationsfunktionen. School: TU Darmstadt, Knowledge Engineering Group. May, , 2011.
BibTeX:
@mastersthesis{ba:Dittmar,
  author = {Dittmar, Denny},
  title = {Ranking von Schach-Evaluationsfunktionen},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2011},
  note = {Bachelor's Thesis},
  url = {http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/bachelor/2011/Dittmar_Denny.pdf}
}
Jon Doyle. Prospects for Preferences. Computational Intelligence, Vol. 20(2), pages 111-136, 2004.
BibTeX:
@article{Preferences-Prospects,
  author = {Doyle, Jon},
  title = {Prospects for Preferences},
  journal = {Computational Intelligence},
  year = {2004},
  volume = {20},
  number = {2},
  pages = {111--136}
}
Cynthia Dwork, Ravi Kumara, Moni Naor and D. Sivakumar. Rank Aggregation Methods for the Web. In Proceedings of the 10th International World Wide Web Conference (WWW-01), Hong Kong, China, pages 613-622, 2001.
BibTeX:
@inproceedings{RankAggregation-Web,
  author = {Dwork, Cynthia and Kumara, Ravi and Naor, Moni and Sivakumar, D.},
  title = {Rank Aggregation Methods for the Web},
  booktitle = {Proceedings of the 10th International World Wide Web Conference (WWW-01)},
  year = {2001},
  pages = {613--622}
}
André Elisseeff and Jason Weston. A Kernel Method for Multi-Labelled Classification. In Advances in Neural Information Processing Systems (Thomas G. Dietterich, Suzanna Becker and Zoubin Ghahramani, editors), Vol. 14, pages 681-687, MIT Press, 2001.
BibTeX:
@inproceedings{RankSVM,
  author = {Elisseeff, Andr{\'{e}} and Weston, Jason},
  title = {A Kernel Method for Multi-Labelled Classification},
  booktitle = {Advances in Neural Information Processing Systems},
  editor = {Dietterich, Thomas G. and Becker, Suzanna and Ghahramani, Zoubin},
  publisher = {MIT Press},
  year = {2001},
  volume = {14},
  pages = {681--687},
  url = {http://books.nips.cc/papers/files/nips14/AA45.pdf}
}
János Fodor and Marc Roubens. Fuzzy Preference Modelling and Multicriteria Decision Support. , Kluwer Academic Publishers, 1994.
BibTeX:
@book{FuzzyPreferenceModelling,
  author = {Fodor, J{\'{a}}nos and Roubens, Marc},
  title = {Fuzzy Preference Modelling and Multicriteria Decision Support},
  publisher = {Kluwer Academic Publishers},
  year = {1994}
}
Eibe Frank and Mark Hall. A Simple Approach to Ordinal Classification. In Proceedings of the 12th European Conference on Machine Learning (ECML-01) (Luc De Raedt and Peter A. Flach, editors), Freiburg, Germany, pages 145-156, Springer-Verlag, 2001.
BibTeX:
@inproceedings{OrderedClasses-Simple,
  author = {Frank, Eibe and Hall, Mark},
  title = {A Simple Approach to Ordinal Classification},
  booktitle = {Proceedings of the 12th European Conference on Machine Learning (ECML-01)},
  editor = {De Raedt, Luc and Flach, Peter A.},
  publisher = {Springer-Verlag},
  year = {2001},
  pages = {145--156}
}
Jerome H. Friedman. Another Approach to Polychotomous Classification. , Stanford, CA, 1996.
BibTeX:
@techreport{PolychotomousClassification-TR,
  author = {Friedman, Jerome H.},
  title = {Another Approach to Polychotomous Classification},
  year = {1996},
  url = {http://www-stat.stanford.edu/~jhf/ftp/poly.ps.Z}
}
Johannes Fürnkranz and Eyke Hüllermeier. Preference Learning and Ranking by Pairwise Comparison. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 65-82, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Fuernkranz,
  author = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  title = {Preference Learning and Ranking by Pairwise Comparison},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {65--82},
  url = {http://www.ke.informatik.tu-darmstadt.de/publications/papers/PLBook-Pairwise.pdf},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Johannes Fürnkranz and Eyke Hüllermeier. Preference Learning: An Introduction. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 1-17, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Introduction,
  author = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  title = {Preference Learning: An Introduction},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {1--17},
  url = {http://www.ke.informatik.tu-darmstadt.de/publications/papers/PLBook-Introduction.pdf},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Johannes Fürnkranz and Eyke Hüllermeier. Preference Learning. In Encyclopedia of the Sciences of Learning (Norbert M. Seel, editors), pages 986, Springer-Verlag, 2012.
BibTeX:
@incollection{jf:PreferenceLearning-EncLearningSciences,
  author = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  title = {Preference Learning},
  booktitle = {Encyclopedia of the Sciences of Learning},
  editor = {Seel, Norbert M.},
  publisher = {Springer-Verlag},
  year = {2012},
  pages = {986}
}
Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng and Sang-Hyeun Park. Preference-based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm. Machine Learning, Vol. 89(1-2), pages 123-156, 2012.
Abstract: This paper makes a first step toward the integration of two subfields of machine learning, namely preference learning and reinforcement learning (RL). An important motivation for a preference-based approach to reinforcement learning is the observation that in many real-world domains, numerical feedback signals are not readily available, or are defined arbitrarily in order to satisfy the needs of conventional RL algorithms. Instead, we propose an alternative framework for reinforcement learning, in which qualitative reward signals can be directly used by the learner. The framework may be viewed as a generalization of the conventional RL framework in which only a partial order between policies is required instead of the total order induced by their respective expected long-term reward. Therefore, building on novel methods for preference learning, our general goal is to equip the RL agent with qualitative policy models, such as ranking functions that allow for sorting its available actions from most to least promising, as well as algorithms for learning such models from qualitative feedback. As a proof of concept, we realize a first simple instantiation of this framework that defines preferences based on utilities observed for trajectories. To that end, we build on an existing method for approximate policy iteration based on roll-outs. While this approach is based on the use of classification methods for generalization and policy learning, we make use of a specific type of preference learning method called label ranking. Advantages of preference-based approximate policy iteration are illustrated by means of two case studies.
BibTeX:
@article{jf:MLJ-PrefBasedRL,
  author = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke and Cheng, Weiwei and Park, Sang-Hyeun},
  title = {Preference-based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm},
  journal = {Machine Learning},
  year = {2012},
  volume = {89},
  number = {1-2},
  pages = {123--156},
  note = {Special Issue of Selected Papers from ECML/PKDD-11},
  doi = {http://dx.doi.org/10.1007/s10994-012-5313-8}
}
Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Słowiński and Scott Sanner. Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports (Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Słowiński and Scott Sanner, editors), Dagstuhl, Germany, Vol. 4(3), pages 1-27, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, 2014.
BibTeX:
@article{frnkranz_et_al:DR:2014:4550,
  author = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke and Rudin, Cynthia and S{\l}owiński, Roman and Sanner, Scott},
  title = {{Preference Learning (Dagstuhl Seminar 14101)}},
  journal = {Dagstuhl Reports},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke and Rudin, Cynthia and S{\l}owiński, Roman and Sanner, Scott},
  publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  year = {2014},
  volume = {4},
  number = {3},
  pages = {1--27},
  url = {http://drops.dagstuhl.de/opus/volltexte/2014/4550},
  doi = {http://dx.doi.org/10.4230/dagrep.4.3.1}
}
Johannes Fürnkranz and Jan Frederik Sima. On Exploiting Hierarchical Label Structure with Pairwise Classifiers. SIGKDD Explorations, Vol. 12(2), pages 21-25, 2010.
BibTeX:
@article{jf:SigKDDExp,
  author = {F{\"{u}}rnkranz, Johannes and Sima, Jan Frederik},
  title = {On Exploiting Hierarchical Label Structure with Pairwise Classifiers},
  journal = {SIGKDD Explorations},
  year = {2010},
  volume = {12},
  number = {2},
  pages = {21--25},
  note = {Special Issue on Mining Unexpected Results},
  url = {http://www.sigkdd.org/explorations/issues/12-2-2010-12/v12-02-6-UR-Fuernkranz.pdf}
}
Mirco Gelain, Maria Silvia Pini, Francesca Rossi, K. Brent Venable and Toby Walsh. Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies. In CP '08: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming, Berlin, Heidelberg, pages 402-417, Springer-Verlag, 2008.
BibTeX:
@inproceedings{1431570,
  author = {Gelain, Mirco and Pini, Maria Silvia and Rossi, Francesca and Venable, K. Brent and Walsh, Toby},
  title = {Elicitation Strategies for Fuzzy Constraint Problems with Missing Preferences: Algorithms and Experimental Studies},
  booktitle = {CP '08: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming},
  publisher = {Springer-Verlag},
  year = {2008},
  pages = {402--417},
  doi = {http://dx.doi.org/10.1007/978-3-540-85958-1\_27}
}
Marco de Gemmis, Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci and Giovanni Semeraro. Learning Preference Models in Recommender Systems. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 387-407, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Gemmis,
  author = {Gemmis, Marco de and Iaquinta, Leo and Lops, Pasquale and Musto, Cataldo and Narducci, Fedelucio and Semeraro, Giovanni},
  title = {Learning Preference Models in Recommender Systems},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {387--407},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Xiubo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li and Heung-Yeung Shum. Query Dependent Ranking Using $K$-Nearest Neighbor. In Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-08) (S. H. Myaeng, D. W. Oard, Fabrizio Sebastiani, T. S. Chua and M. K. Leong, editors), Singapore, pages 115-122, ACM, 2008.
BibTeX:
@inproceedings{QueryDependentRanking-kNN,
  author = {Geng, Xiubo and Liu, Tie-Yan and Qin, Tao and Arnold, Andrew and Li, Hang and Shum, Heung-Yeung},
  title = {Query Dependent Ranking Using $K$-Nearest Neighbor},
  booktitle = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-08)},
  editor = {Myaeng, S. -H. and Oard, D. W. and Sebastiani, Fabrizio and Chua, T. -S. and Leong, M. -K.},
  publisher = {ACM},
  year = {2008},
  pages = {115-122}
}
Joachim Giesen, Klaus Mueller, Bilyana Taneva and Peter Zolliker. Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 297-315, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Giesen,
  author = {Giesen, Joachim and Mueller, Klaus and Taneva, Bilyana and Zolliker, Peter},
  title = {Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {297--315},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Aristides Gionis, Heikki Mannila, Kai Puolamäki and Antti Ukkonen. A Randomized Approximation Algorithm for Computing Bucket Orders. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06) (T. Eliassi-Rad, L. H. Ungar, Mark Craven and Dimitrios Gunopulos, editors), pages 561-566, 2006.
BibTeX:
@inproceedings{BucketOrders,
  author = {Gionis, Aristides and Mannila, Heikki and Puolam{\"{a}}ki, Kai and Ukkonen, Antti},
  title = {A Randomized Approximation Algorithm for Computing Bucket Orders},
  booktitle = {Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-06)},
  editor = {Eliassi-Rad, T. and Ungar, L. H. and Craven, Mark and Gunopulos, Dimitrios},
  year = {2006},
  pages = {561--566}
}
Leo A. Goodman and William H. Kruskal. Measures of Association for Cross Classifications. Journal of the American Statistical Association, Vol. 49(268), pages 133-145, 1954.
BibTeX:
@article{GammaCoefficient,
  author = {Goodman, Leo A. and Kruskal, William H.},
  title = {Measures of Association for Cross Classifications},
  journal = {Journal of the American Statistical Association},
  year = {1954},
  volume = {49},
  number = {268},
  pages = {133--145}
}
Stephen Gordon and Michel Truchon. Social Choice, Optimal Inference and Figure Skating. Social Choice and Welfare, Vol. 30(2), pages 265-284, 2008.
BibTeX:
@article{RankAggregation-FigureSkating,
  author = {Gordon, Stephen and Truchon, Michel},
  title = {Social Choice, Optimal Inference and Figure Skating},
  journal = {Social Choice and Welfare},
  year = {2008},
  volume = {30},
  number = {2},
  pages = {265--284}
}
Salvatore Greco, Milosz Kadzinski, Vincent Mousseau and Roman Słowiński. ELECTRE$^GKMS$: Robust ordinal regression for outranking methods. European Journal of Operational Research, Vol. 214(1), pages 118-135, 2011.
BibTeX:
@article{ELECTRE-GKMS,
  author = {Greco, Salvatore and Kadzinski, Milosz and Mousseau, Vincent and S{\l}owiński, Roman},
  title = {ELECTRE$^{\mbox{GKMS}}$: Robust ordinal regression for outranking methods},
  journal = {European Journal of Operational Research},
  year = {2011},
  volume = {214},
  number = {1},
  pages = {118--135}
}
Salvatore Greco, Benedetto Matarazzo and Roman Słowiński. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, Vol. 129(1), pages 1-47, 2001.
BibTeX:
@article{MCDA-RoughSets,
  author = {Greco, Salvatore and Matarazzo, Benedetto and S{\l}owiński, Roman},
  title = {Rough sets theory for multicriteria decision analysis},
  journal = {European Journal of Operational Research},
  year = {2001},
  volume = {129},
  number = {1},
  pages = {1--47}
}
Vu Ha and Peter Haddawy. Similarity of Personal Preferences: Theoretical Foundations and Empirical Analysis. Artificial Intelligence, Vol. 146, pages 149-173, 2003.
BibTeX:
@article{ha_so03,
  author = {Ha, Vu and Haddawy, Peter},
  title = {Similarity of Personal Preferences: {T}heoretical Foundations and Empirical Analysis},
  journal = {Artificial Intelligence},
  year = {2003},
  volume = {146},
  pages = {149--173}
}
Peter Haddawy, Vu Ha, Angelo Restificar, Benjamin Geisler and Benjamin Miyamoto. Preference Elicitation via Theory Refinement. Journal of Machine Learning Research, Vol. 4, pages 317-337, 2003.
BibTeX:
@article{hadd_pe03,
  author = {Haddawy, Peter and Ha, Vu and Restificar, Angelo and Geisler, Benjamin and Miyamoto, Benjamin},
  title = {Preference Elicitation via Theory Refinement},
  journal = {Journal of Machine Learning Research},
  year = {2003},
  volume = {4},
  pages = {317--337}
}
Sariel Har-Peled, Dan Roth and Dav Zimak. Constraint Classification for Multiclass Classification and Ranking. In Advances in Neural Information Processing Systems 15 (NIPS-02) (S. Becker, S. Thrun and K. Obermayer, editors), pages 785-792, 2003.
BibTeX:
@inproceedings{ConstraintClassification-2,
  author = {Har-Peled, Sariel and Roth, Dan and Zimak, Dav},
  title = {Constraint Classification for Multiclass Classification and Ranking},
  booktitle = {Advances in Neural Information Processing Systems 15 (NIPS-02)},
  editor = {Becker, S. and Thrun, S. and Obermayer, K.},
  year = {2003},
  pages = {785--792}
}
Sariel Har-Peled, Dan Roth and Dav Zimak. Constraint Classification: A New Approach to Multiclass Classification. In Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02) (Nicolò Cesa-Bianchi, M. Numao and R. Reischuk, editors), Lübeck, Germany, pages 365-379, Springer, 2002.
BibTeX:
@inproceedings{ConstraintClassification,
  author = {Har-Peled, Sariel and Roth, Dan and Zimak, Dav},
  title = {Constraint Classification: A New Approach to Multiclass Classification},
  booktitle = {Proceedings of the 13th International Conference on Algorithmic Learning Theory (ALT-02)},
  editor = {Cesa-Bianchi, Nicol{\`{o}} and Numao, M. and Reischuk, R.},
  publisher = {Springer},
  year = {2002},
  pages = {365--379}
}
Trevor Hastie and Robert Tibshirani. Classification by Pairwise Coupling. In Advances in Neural Information Processing Systems 10 (NIPS-97) (Michael I. Jordan, M. J. Kearns and Sara A. Solla, editors), Vol. 10, pages 507-513, MIT Press, 1997.
BibTeX:
@inproceedings{hastie98classification,
  author = {Hastie, Trevor and Tibshirani, Robert},
  title = {Classification by Pairwise Coupling},
  booktitle = {Advances in Neural Information Processing Systems 10 (NIPS-97)},
  editor = {Jordan, Michael I. and Kearns, M. J. and Solla, Sara A.},
  publisher = {MIT Press},
  year = {1997},
  volume = {10},
  pages = {507--513},
  url = {http://www.stanford.edu/~hastie/Papers/nips97.ps}
}
Trevor Hastie and Robert Tibshirani. Classification by Pairwise Coupling. Annals of Statistics, Vol. 26(2), pages 451-471, 1998.
BibTeX:
@article{pref:PairwiseCoupling,
  author = {Hastie, Trevor and Tibshirani, Robert},
  title = {Classification by Pairwise Coupling},
  journal = {Annals of Statistics},
  year = {1998},
  volume = {26},
  number = {2},
  pages = {451--471}
}
Ralf Herbrich, Thore Graepel, Peter Bollmann-Sdorra and Klaus Obermayer. Supervised Learning of Preference Relations. In Proceedings des Fachgruppentreffens Maschinelles Lernen (FGML-98), pages 43-47, 1998.
BibTeX:
@inproceedings{Preferences-SVM,
  author = {Herbrich, Ralf and Graepel, Thore and Bollmann-Sdorra, Peter and Obermayer, Klaus},
  title = {Supervised Learning of Preference Relations},
  booktitle = {Proceedings des Fachgruppentreffens Maschinelles Lernen (FGML-98)},
  year = {1998},
  pages = {43--47}
}
Ralf Herbrich, Thore Graepel and Klaus Obermayer. Large Margin Rank Boundaries for Ordinal Regression. In Advances in Large Margin Classifiers (P. J. Bartlett, Bernhard Schölkopf, D. Schuurmans and Alexander J. Smola, editors), pages 115-132, MIT Press, 2000.
BibTeX:
@incollection{LargeMargin-OrdinalRegression,
  author = {Herbrich, Ralf and Graepel, Thore and Obermayer, Klaus},
  title = {Large Margin Rank Boundaries for Ordinal Regression},
  booktitle = {Advances in Large Margin Classifiers},
  editor = {Bartlett, P. J. and Sch{\"{o}}lkopf, Bernhard and Schuurmans, D. and Smola, Alexander J.},
  publisher = {MIT Press},
  year = {2000},
  pages = {115--132}
}
Ralf Herbrich, Tom Minka and Thore Graepel. TrueSkill$^TM$: A Bayesian Skill Rating System. In Advances in Neural Information Processing Systems 19 (NIPS-06) (Bernhard Schölkopf, John C. Platt and T. Hoffman, editors), pages 569-576, MIT Press, 2007.
BibTeX:
@inproceedings{TrueSkill,
  author = {Herbrich, Ralf and Minka, Tom and Graepel, Thore},
  title = {TrueSkill$^{\mbox{TM}}$: A Bayesian Skill Rating System},
  booktitle = {Advances in Neural Information Processing Systems 19 (NIPS-06)},
  editor = {Sch{\"{o}}lkopf, Bernhard and Platt, John C. and Hoffman, T.},
  publisher = {MIT Press},
  year = {2007},
  pages = {569--576},
  url = {http://books.nips.cc/papers/files/nips19/NIPS2006_0688.pdf}
}
Chih-Wei Hsu and Chih-Jen Lin. A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks, Vol. 13(2), pages 415-425, 2002.
BibTeX:
@article{hsu01comparison,
  author = {Hsu, Chih-Wei and Lin, Chih-Jen},
  title = {A Comparison of Methods for Multi-class Support Vector Machines},
  journal = {IEEE Transactions on Neural Networks},
  year = {2002},
  volume = {13},
  number = {2},
  pages = {415--425},
  url = {http://www.csie.ntu.edu.tw/~cjlin/papers/multisvm.pdf}
}
Jens C. Hühn and Eyke Hüllermeier. Is an ordinal class structure useful in classifier learning?. International Journal on Data Mining, Modelling and Management, Vol. 1(1), pages 45-67, 2008.
BibTeX:
@article{OrderedClasses-Utility,
  author = {H{\"{u}}hn, Jens C. and H{\"{u}}llermeier, Eyke},
  title = {Is an ordinal class structure useful in classifier learning?},
  journal = {International Journal on Data Mining, Modelling and Management},
  year = {2008},
  volume = {1},
  number = {1},
  pages = {45--67},
  doi = {http://dx.doi.org/10.1504/ijdmmm.2008.022537}
}
Eyke Hüllermeier and Klaus Brinker. Learning Valued Preference Structures for Solving Classification Problems. Fuzzy Sets and Systems, Vol. 159(18), pages 2337-2352, 2008.
BibTeX:
@article{ValuedPreferenceStructures,
  author = {H{\"{u}}llermeier, Eyke and Brinker, Klaus},
  title = {Learning Valued Preference Structures for Solving Classification Problems},
  journal = {Fuzzy Sets and Systems},
  year = {2008},
  volume = {159},
  number = {18},
  pages = {2337--2352},
  url = {http://www.mathematik.uni-marburg.de/~eyke/publications/fss08.pdf}
}
Eyke Hüllermeier and Johannes Fürnkranz. Learning from Label Preferences. In Proceedings of the 14th International Conference on Discovery Science (DS-11) (Tapio Elomaa, Jaakko Hollmén and Heikki Mannila, editors), Espoo, Finland, Vol. 6926, pages 2-17, Springer, 2011.
BibTeX:
@inproceedings{jf:DS-11-Invited,
  author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  title = {Learning from Label Preferences},
  booktitle = {Proceedings of the 14th International Conference on Discovery Science (DS-11)},
  editor = {Elomaa, Tapio and Hollm{\'{e}}n, Jaakko and Mannila, Heikki},
  publisher = {Springer},
  year = {2011},
  volume = {6926},
  pages = {2--17},
  doi = {http://dx.doi.org/10.1007/978-3-642-24477-3_2}
}
Eyke Hüllermeier and Johannes Fürnkranz. Learning from Label Preferences. In Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT-11) (Jyrki Kivinen, Csaba Szepesvári, Esko Ukkonen and Thomas Zeugmann, editors), Espoo, Finland, Vol. 6925, pages 38, Springer, 2011.
BibTeX:
@inproceedings{jf:ALT-11,
  author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  title = {Learning from Label Preferences},
  booktitle = {Proceedings of the 22nd International Conference on Algorithmic Learning Theory (ALT-11)},
  editor = {Kivinen, Jyrki and Szepesv{\'{a}}ri, Csaba and Ukkonen, Esko and Zeugmann, Thomas},
  publisher = {Springer},
  year = {2011},
  volume = {6925},
  pages = {38},
  note = {Extended Abstract},
  doi = {http://dx.doi.org/10.1007/978-3-642-24412-4_5}
}
Eyke Hüllermeier and Johannes Fürnkranz. Editorial: Preference Learning and Ranking. Machine Learning, Vol. 93(2-3), pages 185-189, 2013.
BibTeX:
@article{jf:MLJ-SI-Preferences-Editorial,
  author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  title = {Editorial: Preference Learning and Ranking},
  journal = {Machine Learning},
  year = {2013},
  volume = {93},
  number = {2-3},
  pages = {185--189},
  url = {http://www.springer.com/alert/urltracking.do?id=L35fd080Md1c8edSad2177d}
}
Eyke Hüllermeier and Johannes Fürnkranz. On Predictive Accuracy and Risk Minimization in Pairwise Label Ranking. Journal of Computer and System Sciences February, , Vol. 76(1), pages 49-62, 2010.
BibTeX:
@article{jf:JCSS,
  author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  title = {On Predictive Accuracy and Risk Minimization in Pairwise Label Ranking},
  journal = {Journal of Computer and System Sciences},
  year = {2010},
  volume = {76},
  number = {1},
  pages = {49--62},
  doi = {http://dx.doi.org/10.1016/j.jcss.2009.05.005}
}
Eyke Hüllermeier and Stijn Vanderlooy. Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting.. Pattern Recognition January, , Vol. 43(1), pages 128-142, 2010.
Abstract: dblp
BibTeX:
@article{hullermeier10aggregation,
  author = {H{\"{u}}llermeier, Eyke and Vanderlooy, Stijn},
  title = {Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting.},
  journal = {Pattern Recognition},
  year = {2010},
  volume = {43},
  number = {1},
  pages = {128-142},
  url = {http://www.mathematik.uni-marburg.de/~eyke/publications/PR09draft.pdf},
  doi = {http://dx.doi.org/10.1016/j.patcog.2009.06.013}
}
Aiwen Jiang, Chunheng Wang and Yuanping Zhu. Calibrated Rank-SVM for Multi-Label Image Categorization. In Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08), Hong Kong, pages 1450-1455, 2008.
BibTeX:
@inproceedings{CalibratedRankSVM,
  author = {Jiang, Aiwen and Wang, Chunheng and Zhu, Yuanping},
  title = {Calibrated {Rank-SVM} for Multi-Label Image Categorization},
  booktitle = {Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IJCNN-08)},
  year = {2008},
  pages = {1450--1455}
}
Thorsten Joachims. Optimizing Search Engines Using Clickthrough Data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-02), pages 133-142, ACM Press, 2002.
BibTeX:
@inproceedings{OptimizingSearchEngines-Clickthrough,
  author = {Joachims, Thorsten},
  title = {Optimizing Search Engines Using Clickthrough Data},
  booktitle = {Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-02)},
  publisher = {ACM Press},
  year = {2002},
  pages = {133--142},
  doi = {http://dx.doi.org/10.1145/775047.775067}
}
Thorsten Joachims, Laura Granka, Bing Pan, Helene Hembrooke and Geri Gay. Accurately Interpreting Clickthrough Data as Implicit Feedback. In Proceedings of the 28th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR-05), pages 154-161, 2005.
BibTeX:
@inproceedings{Clickthrough-Feedback,
  author = {Joachims, Thorsten and Granka, Laura and Pan, Bing and Hembrooke, Helene and Gay, Geri},
  title = {Accurately Interpreting Clickthrough Data as Implicit Feedback},
  booktitle = {Proceedings of the 28th Annual International ACM Conference on Research and Development in Information Retrieval (SIGIR-05)},
  year = {2005},
  pages = {154--161}
}
Aimable Robert Jonckheere. A distribution-free k-sample test against ordered alternatives. Biometrika, Vol. 41, pages 133–-145, 1954.
BibTeX:
@article{JonckheereTerpstra,
  author = {Jonckheere, Aimable Robert},
  title = {A distribution-free k-sample test against ordered alternatives},
  journal = {Biometrika},
  year = {1954},
  volume = {41},
  pages = {133–-145}
}
Kalervo Järvelin and Jaana Kekäläinen. Cumulated Gain-based Evaluation of IR Techniques. ACM Transactions on Information Systems, Vol. 20(4), pages 422-446, 2002.
BibTeX:
@article{NDCG,
  author = {J{\"{a}}rvelin, Kalervo and Kek{\"{a}}l{\"{a}}inen, Jaana},
  title = {Cumulated Gain-based Evaluation of {IR} Techniques},
  journal = {ACM Transactions on Information Systems},
  year = {2002},
  volume = {20},
  number = {4},
  pages = {422--446}
}
Souhila Kaci. Working with Preferences: Less Is More. , Springer, 2011.
BibTeX:
@book{Preferences-LessIsMore,
  author = {Kaci, Souhila},
  title = {Working with Preferences: Less Is More},
  publisher = {Springer},
  year = {2011}
}
Toshihiro Kamishima and Shotaro Akaho. Dimension Reduction for Object Ranking. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 203-215, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Kamishima-2,
  author = {Kamishima, Toshihiro and Akaho, Shotaro},
  title = {Dimension Reduction for Object Ranking},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {203--215},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Toshihiro Kamishima and Shotaro Akaho. Efficient Clustering for Orders. In Mining Complex Data (Djamel A. Zighed, Shusaku Tsumoto, Zbigniew W. Ras and Hakim Hacid, editors), Vol. 165, pages 261-279, Springer-Verlag, 2009.
BibTeX:
@incollection{SushiData,
  author = {Kamishima, Toshihiro and Akaho, Shotaro},
  title = {Efficient Clustering for Orders},
  booktitle = {Mining Complex Data},
  editor = {Zighed, Djamel A. and Tsumoto, Shusaku and Ras, Zbigniew W. and Hacid, Hakim},
  publisher = {Springer-Verlag},
  year = {2009},
  volume = {165},
  pages = {261--279}
}
Toshihiro Kamishima, Hideto Kazawa and Shotaro Akaho. A Survey and Empirical Comparison of Object Ranking Methods. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 181-201, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Kamishima-1,
  author = {Kamishima, Toshihiro and Kazawa, Hideto and Akaho, Shotaro},
  title = {A Survey and Empirical Comparison of Object Ranking Methods},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {181--201},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Alexandros Karatzoglou and Markus Weimer. Collaborative Preference Learning. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 409-427, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Karatzoglou,
  author = {Karatzoglou, Alexandros and Weimer, Markus},
  title = {Collaborative Preference Learning},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {409--427},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Aldebaro Klautau, Nikola Jevtić and Alon Orlitsky. On Nearest-Neighbor ECOC with Application to All-Pairs Multiclass SVM. Journal of Machine Learning Research, Vol. 4, pages 1-15, 2003.
BibTeX:
@article{NN-ECOC-Pairwise-SVM,
  author = {Klautau, Aldebaro and Jevti{\'{c}}, Nikola and Orlitsky, Alon},
  title = {On Nearest-Neighbor {ECOC} with Application to All-Pairs Multiclass {SVM}},
  journal = {Journal of Machine Learning Research},
  year = {2003},
  volume = {4},
  pages = {1--15}
}
Stefan Knerr, Léon Personnaz and Gérard Dreyfus. Single-Layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network. In Neurocomputing: Algorithms, Architectures and Applications (F. Fogelman Soulié and J. Hérault, editors), Vol. F68, pages 41-50, Springer-Verlag, 1990.
BibTeX:
@incollection{StepwiseNN,
  author = {Knerr, Stefan and Personnaz, L{\'{e}}on and Dreyfus, G{\'{e}}rard},
  title = {Single-Layer Learning Revisited: A Stepwise Procedure for Building and Training a Neural Network},
  booktitle = {Neurocomputing: Algorithms, Architectures and Applications},
  editor = {Fogelman Souli{\'{e}}, F. and H{\'{e}}rault, J.},
  publisher = {Springer-Verlag},
  year = {1990},
  volume = {F68},
  pages = {41--50}
}
Stefan Knerr, Léon Personnaz and Gérard Dreyfus. Handwritten Digit Recognition by Neural Networks with Single-Layer Training. IEEE Transactions on Neural Networks, Vol. 3(6), pages 962-968, 1992.
BibTeX:
@article{StepwiseNN-DigitRecognition,
  author = {Knerr, Stefan and Personnaz, L{\'{e}}on and Dreyfus, G{\'{e}}rard},
  title = {Handwritten Digit Recognition by Neural Networks with Single-Layer Training},
  journal = {IEEE Transactions on Neural Networks},
  year = {1992},
  volume = {3},
  number = {6},
  pages = {962--968}
}
Stefan Kramer, Gerhard Widmer, Bernhard Pfahringer and Michael DeGroeve. Prediction of Ordinal Classes Using Regression Trees. Fundamenta Informaticae, Vol. XXI, pages 1001-1013, 2001.
BibTeX:
@article{OrderedClasses,
  author = {Kramer, Stefan and Widmer, Gerhard and Pfahringer, Bernhard and DeGroeve, Michael},
  title = {Prediction of Ordinal Classes Using Regression Trees},
  journal = {Fundamenta Informaticae},
  year = {2001},
  volume = {XXI},
  pages = {1001--1013}
}
Ulrich H. G. Kreßel. Pairwise Classification and Support Vector Machines. In Advances in Kernel Methods: Support Vector Learning (Bernhard Schölkopf, C. J. C. Burges and Alexander J. Smola, editors), Cambridge, MA, pages 255-268, MIT Press, 1999.
BibTeX:
@incollection{PairwiseClassification,
  author = {Kre{\ss}el, Ulrich H. -G.},
  title = {Pairwise Classification and Support Vector Machines},
  booktitle = {Advances in Kernel Methods: Support Vector Learning},
  editor = {Sch{\"{o}}lkopf, Bernhard and Burges, C. J. C. and Smola, Alexander J.},
  publisher = {MIT Press},
  year = {1999},
  pages = {255--268}
}
Jérôme Lang, Maria Silvia Pini, Francesca Rossi, Kristen Brent Venable and Toby Walsh. Winner Determination in Sequential Majority Voting. In IJCAI (Manuela M. Veloso, editors), pages 1372-1377, 2007.
BibTeX:
@inproceedings{DBLP:conf/ijcai/LangPRVW07,
  author = {Lang, J{\'{e}}r{\^{o}}me and Pini, Maria Silvia and Rossi, Francesca and Venable, Kristen Brent and Walsh, Toby},
  title = {Winner Determination in Sequential Majority Voting},
  booktitle = {IJCAI},
  editor = {Veloso, Manuela M.},
  year = {2007},
  pages = {1372-1377},
  url = {http://dli.iiit.ac.in/ijcai/IJCAI-2007/PDF/IJCAI07-221.pdf}
}
Ji-Ung Lee. Transductive Pairwise Classification. School: TU Darmstadt, Knowledge Engineeering Group., 2013.
BibTeX:
@mastersthesis{ba:Lee,
  author = {Lee, Ji-Ung},
  title = {Transductive Pairwise Classification},
  school = {TU Darmstadt, Knowledge Engineeering Group},
  year = {2013},
  note = {Bachelor's Thesis},
  url = {http://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2013/Lee_JiUng.pdf}
}
Tie-Yan Liu. Learning to Rank for Information Retrieval. , Springer, 2011.
BibTeX:
@book{LETOR-Book,
  author = {Liu, Tie-Yan},
  title = {Learning to Rank for Information Retrieval},
  publisher = {Springer},
  year = {2011}
}
Tie-Yan Liu. Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval, Vol. 3(3), pages 225-331, 2009.
BibTeX:
@article{LETOR-IR,
  author = {Liu, Tie-Yan},
  title = {Learning to Rank for Information Retrieval},
  journal = {Foundations and Trends in Information Retrieval},
  year = {2009},
  volume = {3},
  number = {3},
  pages = {225--331}
}
Tie-Yan Liu, Thorsten Joachims, Hang Li and Chengxiang Zhai. Introduction to special issue on learning to rank for information retrieval. Inf. Retr., Vol. 13(3), pages 197-200, 2010.
BibTeX:
@article{DBLP:journals/ir/LiuJLZ10,
  author = {Liu, Tie-Yan and Joachims, Thorsten and Li, Hang and Zhai, Chengxiang},
  title = {Introduction to special issue on learning to rank for information retrieval},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {197-200},
  doi = {http://dx.doi.org/10.1007/s10791-009-9120-1}
}
Ana Carolina Lorena, André Carlos Ponce Leon Ferreira de Carvalho and João Gama. A review on the combination of binary classifiers in multiclass problems. Artificial Intelligence Review, Vol. 30(1-4), pages 19-37, 2008.
BibTeX:
@article{Binary-Multiclass-Review,
  author = {Lorena, Ana Carolina and de Carvalho, Andr{\'{e}} Carlos Ponce Leon Ferreira and Gama, Jo{\~{a}}o},
  title = {A review on the combination of binary classifiers in multiclass problems},
  journal = {Artificial Intelligence Review},
  year = {2008},
  volume = {30},
  number = {1-4},
  pages = {19--37},
  doi = {http://dx.doi.org/10.1007/s10462-009-9114-9}
}
Eneldo Loza Menca. Multilabel Classification in Parallel Tasks. In Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010 (Min-Ling Zhang, Grigorios Tsoumakas and Zhi-Hua Zhou, editors) June, , pages 29-36, 2010.
Abstract: In real world multilabel problems, it is often the case that e.g. documents are simultaneously classified with labels from multiple domains, such as genres in addition to topics. In practice, each of these problems is solved independently without taking advantage of possible label correlations between domains. Following the multi-task learning setting, in which multiple similar tasks are learned in parallel, we propose a global learning approach that jointly considers all domains. It is empirically demonstrated in this work that this approach is effective despite its simplicity when using a multilabel learner that takes label correlations into account.
BibTeX:
@inproceedings{loza10pt,
  author = {Loza Menc{\'{\i}}a, Eneldo},
  title = {Multilabel Classification in Parallel Tasks},
  booktitle = {Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010},
  editor = {Zhang, Min-Ling and Tsoumakas, Grigorios and Zhou, Zhi-Hua},
  year = {2010},
  pages = {29-36},
  url = {http://www.ke.tu-darmstadt.de/publications/papers/loza10mlpt.pdf}
}
Eneldo Loza Menca. Efficient Pairwise Multilabel Classification. School: Technische Universität Darmstadt, Knowledge Engineering Group. July, , 2012.
Abstract: Multilabel classification learning is the task of learning a mapping between objects and sets of possibly overlapping classes and has gained increasing attention in recent times. A prototypical application scenario for multilabel classification is the assignment of a set of keywords to a document, a frequently encountered problem in the text classification domain. With upcoming Web 2.0 technologies, this domain is extended by a wide range of tag suggestion tasks and the trend definitely is moving towards more data points and more labels. This work provides an extended introduction into the topic of multilabel classification, a detailed formalization and a comprehensive overview of the present state-of-the-art approaches.
A commonly used solution for solving multilabel tasks is to decompose the original problem into several subproblems. These subtasks are usually easy to solve with conventional techniques. In contrast to the straightforward approach of training one classifier for independently predicting the relevance of each class (binary relevance), this work focuses particularly on the pairwise decomposition of the original problem in which a decision function is learned for each possible pair of classes. The main advantage of this approach, the improvement of the predictive quality, comes at the cost of its main disadvantage, the quadratic number of classifiers needed (with respect to the number of labels). This thesis presents a framework of efficient and scalable solutions for handling hundreds or thousands of labels despite the quadratic dependency.
As it turns out, training such a pairwise ensemble of classifiers can be accomplished in linear time and only differs from the straightforward binary relevance approach (BR) by a factor relative to the average number of labels associated to an object, which is usually small. Furthermore, the integration of a smart scheduling technique inspired from sports tournaments safely reduces the quadratic number of base classifier evaluations to log-linear in practice. Combined with a simple yet fast and powerful learning algorithm for linear classifiers, data with a huge number of high dimensional points, which was not amenable to pairwise learning before, can be processed even under real-time conditions.
The remaining bottleneck, the exploding memory requirements, is coped by taking advantage of an interesting property of linear classifiers, namely the possibility of dual reformulation as a linear combination of the training examples. The suitability is demonstrated on the novel EUR-Lex text collection, which particularly puts the main scalability issue of pairwise learning to test. With its almost 4,000 labels and 20,000 documents it is one of the most challenging test beds in multilabel learning to date. The dual formulation allows to maintain the mathematical equivalent to 8 million base learners needed for conventionally solving EUR-Lex in almost the same amount of space as binary relevance. Moreover, BR was clearly beaten in the experiments.
A further contribution based on hierarchical decomposition and arrangement of the original problem allows to reduce the dependency on the number of labels to even sub-linearity. This approach opens the door to a wide range of new challenges and applications but simultaneously maintains the advantages of pairwise learning, namely the excellent predictive quality. It was even shown in comparison to the flat variant that it has a particularly positive effect on balancing recall and precision on datasets with a large number of labels.
The improved scalability and efficiency allowed to apply pairwise classification to a set of large multilabel problems with a parallel base of data points but different domains of labels. A first attempt was made in this parallel tasks setting in order to investigate the exploitation of label dependencies by pairwise learning, with first encouraging results. The usage of multilabel learning techniques for the automatic annotation of texts constitutes a further obvious but so far missing connection to multi-task and multi-target learning. The presented solution considers the simultaneous tagging of words with different but possibly overlapping annotation schemes as a multilabel problem. This solution is expected to particularly benefit from approaches which exploit label dependencies. The ability of pairwise learning for this purpose is obviously restricted to pairwise relations, therefore a technique is investigated which explores label constellations that only exist locally for a subgroup of data points. In addition to the positive effect of the supplemental information, the experimental evaluation demonstrates an interesting insight with regards to the different behavior of several state-of-the-art approaches with respect to the optimization of particular multilabel measures, a controversial topic in multilabel classification.
BibTeX:
@phdthesis{loza2012diss,
  author = {Loza Menc{\'{\i}}a, Eneldo},
  title = {Efficient Pairwise Multilabel Classification},
  school = {Technische Universität Darmstadt, Knowledge Engineering Group},
  year = {2012},
  note = {eingereicht am 10.06.2012, Verteidigung am 24.07.2012},
  url = {http://tuprints.ulb.tu-darmstadt.de/3226/7/loza12diss.pdf}
}
Eneldo Loza Menca. Segmentation of legal documents. In Proceedings of the 12th International Conference on Artificial Intelligence and Law, New York, NY, USA June, , pages 88-97, ACM, 2009.
Abstract: An overwhelming number of legal documents is available in digital form. However, most of the texts are usually only provided in a semi-structured form, i.e. the documents are structured only implicitly using text formatting and alignment. In this form the documents are perfectly understandable by a human, but not by a machine. This is an obstacle towards advanced intelligent legal information retrieval and knowledge systems. The reason for this lack of structured knowledge is that the conversion of texts in conventional form into a structured, machine-readable form, a process called segmentation, is frequently done manually and is therefore very expensive. We introduce a trainable system based on state-of-the-art Information Extraction techniques for the automatic segmentation of legal documents. Our system makes special use of the implicitly given structure in the source digital file as well as of the explicit knowledge about the target structure. Our evaluation on the French IPR Law demonstrates that the system is able to learn an effective segmenter given only a few manually processed training documents. In some cases, even only one seen example is sufficient in order to correctly process the remaining documents.
BibTeX:
@inproceedings{loza09segmentation,
  author = {Loza Menc{\'{\i}}a, Eneldo},
  title = {Segmentation of legal documents},
  booktitle = {Proceedings of the 12th International Conference on Artificial Intelligence and Law},
  publisher = {ACM},
  year = {2009},
  pages = {88--97},
  url = {http://www.ke.tu-darmstadt.de/publications/papers/loza09segmentation.pdf},
  doi = {http://dx.doi.org/10.1145/1568234.1568245}
}
Eneldo Loza Menca. Paarweises Lernen von Multilabel-Klassifikationen mit dem Perzeptron-Algorithmus. School: TU Darmstadt, Knowledge Engineering Group. March, , 2006.
BibTeX:
@mastersthesis{da:Loza,
  author = {Loza Menc{\'{\i}}a, Eneldo},
  title = {Paarweises Lernen von Multilabel-Klassifikationen mit dem Perzeptron-Algorithmus},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2006},
  note = {Diplomarbeit},
  url = {http://www.ke.tu-darmstadt.de/lehre/arbeiten/diplom/2006/Loza_Eneldo.pdf}
}
Eneldo Loza Menca and Johannes Fürnkranz. Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. In Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language (Enrico Francesconi, Simonetta Montemagni, Wim Peters and Daniela Tiscornia, editors) May, , Vol. 6036, pages 192-215, Springer-Verlag, 2010.
Abstract: In this paper we apply multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. For this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks.
BibTeX:
@incollection{jf:SemanticLaw,
  author = {Loza Menc{\'{\i}}a, Eneldo and F{\"{u}}rnkranz, Johannes},
  title = {Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain},
  booktitle = {Semantic Processing of Legal Texts -- Where the Language of Law Meets the Law of Language},
  editor = {Francesconi, Enrico and Montemagni, Simonetta and Peters, Wim and Tiscornia, Daniela},
  publisher = {Springer-Verlag},
  year = {2010},
  volume = {6036},
  pages = {192-215},
  edition = {1},
  note = {accompanying EUR-Lex dataset available at http://www.ke.tu-darmstadt.de/resources/eurlex},
  url = {http://www.ke.tu-darmstadt.de/publications/papers/loza10eurlex.pdf},
  doi = {http://dx.doi.org/10.1007/978-3-642-12837-0_11}
}
Eneldo Loza Menca, Sang-Hyeun Park and Johannes Fürnkranz. Efficient Voting Prediction for Pairwise Multilabel Classification. Neurocomputing March, , Vol. 73(7-9), pages 1164 - 1176, 2010.
Abstract: The pairwise approach to multilabel classification reduces the problem to learning and aggregating preference predictions among the possible labels. A key problem is the need to query a quadratic number of preferences for making a prediction. To solve this problem, we extend the recently proposed QWeighted algorithm for efficient pairwise multiclass voting to the multilabel setting, and evaluate the adapted algorithm on several real-world datasets. We achieve an average-case reduction of classifier evaluations from n^2 to n + n d log n, where n is the total number of possible labels and d is the average number of labels per instance, which is typically quite small in real-world datasets.
BibTeX:
@article{jf:Neurocomputing,
  author = {Loza Menc{\'{\i}}a, Eneldo and Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
  title = {Efficient Voting Prediction for Pairwise Multilabel Classification},
  journal = {Neurocomputing},
  year = {2010},
  volume = {73},
  number = {7-9},
  pages = {1164 - 1176},
  url = {http://www.ke.tu-darmstadt.de/publications/papers/neucom10.pdf},
  doi = {http://dx.doi.org/10.1016/j.neucom.2009.11.024}
}
Craig Macdonald, Rodrygo L. T. Santos and Iadh Ounis. On the usefulness of query features for learning to rank. In Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM-12) (Xue wen Chen, Guy Lebanon, Haixun Wang and Mohammed J. Zaki, editors), Maui, HI, pages 2559-2562, ACM, 2012.
BibTeX:
@inproceedings{QueryFeatures,
  author = {Macdonald, Craig and Santos, Rodrygo L. T. and Ounis, Iadh},
  title = {On the usefulness of query features for learning to rank},
  booktitle = {Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM-12)},
  editor = {Chen, Xue-wen and Lebanon, Guy and Wang, Haixun and Zaki, Mohammed J.},
  publisher = {ACM},
  year = {2012},
  pages = {2559--2562}
}
Henry Berthold Mann and Donald Ransom Whitney. On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, Vol. 18, pages 50–-60, 1947.
BibTeX:
@article{MannWhitney,
  author = {Mann, Henry Berthold and Whitney, Donald Ransom},
  title = {On a test of whether one of two random variables is stochastically larger than the other},
  journal = {Annals of Mathematical Statistics},
  year = {1947},
  volume = {18},
  pages = {50–-60}
}
John I. Marden. Analyzing and Modeling Rank Data. , Chapman & Hall, 1995.
BibTeX:
@book{AnalyzingRankData,
  author = {Marden, John I.},
  title = {Analyzing and Modeling Rank Data},
  publisher = {Chapman & Hall},
  year = {1995}
}
Eddy Mayoraz and Ethem Alpaydin. Support Vector Machines for Multi-Class Classification. In Engineering Applications of Bio-Inspired Artificial Neural Networks: Proceedings of the International Work-Conference on Artificial and Natural Neural Networks (IWANN-99), Volume II (J. Mira and J. V. Sánchez-Andrés, editors), Alicante, Spain, pages 833-842, Springer-Verlag, 1999.
BibTeX:
@inproceedings{MultiClass-SVM,
  author = {Mayoraz, Eddy and Alpaydin, Ethem},
  title = {Support Vector Machines for Multi-Class Classification},
  booktitle = {Engineering Applications of Bio-Inspired Artificial Neural Networks: Proceedings of the International Work-Conference on Artificial and Natural Neural Networks (IWANN-99), Volume II},
  editor = {Mira, J. and S{\'{a}}nchez-Andr{\'{e}}s, J. V.},
  publisher = {Springer-Verlag},
  year = {1999},
  pages = {833--842}
}
Eddy Mayoraz and Miguel Moreira. On the Decomposition of Polychotomies into Dichotomies. In Proceedings of the 14th International Conference on Machine Learning (ICML-97), Nashville, TN, pages 219-226, Morgan Kaufmann, 1997.
BibTeX:
@inproceedings{PolychotomyDecomposition,
  author = {Mayoraz, Eddy and Moreira, Miguel},
  title = {On the Decomposition of Polychotomies into Dichotomies},
  booktitle = {Proceedings of the 14th International Conference on Machine Learning (ICML-97)},
  publisher = {Morgan Kaufmann},
  year = {1997},
  pages = {219--226}
}
Miguel Moreira and Eddy Mayoraz. Improved Pairwise Coupling Classification with Correcting Classifiers. In Proceedings of the 10th European Conference on Machine Learning (ECML-98) (C. Nédellec and C. Rouveirol, editors), Chemnitz, Germany, pages 160-171, Springer-Verlag, 1998.
BibTeX:
@inproceedings{PairwiseClassification-Correcting,
  author = {Moreira, Miguel and Mayoraz, Eddy},
  title = {Improved Pairwise Coupling Classification with Correcting Classifiers},
  booktitle = {Proceedings of the 10th European Conference on Machine Learning (ECML-98)},
  editor = {N{\'{e}}dellec, C. and Rouveirol, C.},
  publisher = {Springer-Verlag},
  year = {1998},
  pages = {160--171}
}
Ge Hyun Nam. Ordered Pairwise Classification. School: TU Darmstadt, Knowledge Engineering Group., 2007.
BibTeX:
@mastersthesis{da:Nam,
  author = {Nam, Ge Hyun},
  title = {Ordered Pairwise Classification},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2007},
  note = {Diplom},
  url = {http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/diplom/2007/Nam_GeHyun.pdf}
}
Andrew Y. Ng and Stuart J. Russell. Algorithms for inverse reinforcement learning. In Proceedings of the 17th International Conference on Machine Learning (ICML-00) (Pat Langley, editors), Stanford, CA, pages 663-670, Morgan Kaufmann, 2000.
BibTeX:
@inproceedings{ng_af00,
  author = {Ng, Andrew Y. and Russell, Stuart J.},
  title = {Algorithms for inverse reinforcement learning},
  booktitle = {Proceedings of the 17th International Conference on Machine Learning (ICML-00)},
  editor = {Langley, Pat},
  publisher = {Morgan Kaufmann},
  year = {2000},
  pages = {663--670}
}
Weijian Ni, Yalou Huang and Maoqiang Xie. A Query Dependent Approach to Learning to Rank for Information Retrieval. In Proceedings of the 9thh International Conference on Web-Age Information Management (WAIM-08), Zhangjiajie, China, pages 262-269, IEEE, 2008.
BibTeX:
@inproceedings{QueryDependentRanking-IR,
  author = {Ni, Weijian and Huang, Yalou and Xie, Maoqiang},
  title = {A Query Dependent Approach to Learning to Rank for Information Retrieval},
  booktitle = {Proceedings of the 9thh International Conference on Web-Age Information Management (WAIM-08)},
  publisher = {IEEE},
  year = {2008},
  pages = {262-269}
}
Sang-Hyeun Park. Effiziente Klassifikation und Ranking mit paarweisen Vergleichen. School: TU Darmstadt, Knowledge Engineering Group. December, , 2006.
BibTeX:
@mastersthesis{da:Park,
  author = {Park, Sang-Hyeun},
  title = {Effiziente Klassifikation und Ranking mit paarweisen Vergleichen},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2006},
  note = {Diplom},
  url = {http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/diplom/2006/Park_Sang-Hyeun.pdf}
}
Sang-Hyeun Park and Johannes Fürnkranz. Efficient Implementation of Class-Based Decomposition Schemes for Naive Bayes. Machine Learning, Vol. 96(3), pages 295-309, 2014.
BibTeX:
@article{jf:MLJ-NaiveECOC,
  author = {Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
  title = {Efficient Implementation of Class-Based Decomposition Schemes for Naive Bayes},
  journal = {Machine Learning},
  year = {2014},
  volume = {96},
  number = {3},
  pages = {295--309},
  note = {Technical Note}
}
Sang-Hyeun Park and Johannes Fürnkranz. Efficient prediction algorithms for binary decomposition techniques. Data Mining and Knowledge Discovery, Vol. 24(1), pages 40-77, Springer Netherlands, 2012.
Abstract: Binary decomposition methods transform multiclass learning problems into a series of two-class learning problems that can be solved with simpler learning algorithms. As the number of such binary learning problems often grows super-linearly with the number of classes, we need efficient methods for computing the predictions. In this article, we discuss an efficient algorithm that queries only a dynamically determined subset of the trained classifiers, but still predicts the same classes that would have been predicted if all classifiers had been queried. The algorithm is first derived for the simple case of pairwise classification, and then generalized to arbitrary pairwise decompositions of the learning problem in the form of ternary error-correcting output codes under a variety of different code designs and decoding strategies.
BibTeX:
@article{jf:DAMI,
  author = {Park, Sang-Hyeun and F{\"{u}}rnkranz, Johannes},
  title = {Efficient prediction algorithms for binary decomposition techniques},
  journal = {Data Mining and Knowledge Discovery},
  publisher = {Springer Netherlands},
  year = {2012},
  volume = {24},
  number = {1},
  pages = {40-77},
  url = {http://www.ke.tu-darmstadt.de/publications/papers/dami12.pdf},
  doi = {http://dx.doi.org/10.1007/s10618-011-0219-9}
}
Philip Paulsen and Johannes Fürnkranz. A Moderately Successful Attempt to Train Chess Evaluation Functions of Different Strengths. In Proceedings of the ICML-10 Workshop on Machine Learning and Games (Christian Thurau, Kurt Driessens and Olana Missura, editors), Haifa, Israel, 2010.
BibTeX:
@inproceedings{jf:ICML-10-Games,
  author = {Paulsen, Philip and F{\"{u}}rnkranz, Johannes},
  title = {A Moderately Successful Attempt to Train Chess Evaluation Functions of Different Strengths},
  booktitle = {Proceedings of the ICML-10 Workshop on Machine Learning and Games},
  editor = {Thurau, Christian and Driessens, Kurt and Missura, Olana},
  year = {2010}
}
Philip Paulsen and Johannes Fürnkranz. A Moderately Successful Attempt to Train Chess Evaluation Functions of Different Strengths. June, (TUD-KE-2010-07), 2010.
BibTeX:
@techreport{TUD-KE-2010-07,
  author = {Paulsen, Philip and F{\"{u}}rnkranz, Johannes},
  title = {A Moderately Successful Attempt to Train Chess Evaluation Functions of Different Strengths},
  year = {2010},
  number = {TUD-KE-2010-07},
  url = {http://www.ke.tu-darmstadt.de/publications/reports/tud-ke-2010-07.pdf}
}
Maria Silvia Pini, Francesca Rossi, Kristen Brent Venable and Toby Walsh. Dealing with Incomplete Agents' Preferences and an Uncertain Agenda in Group Decision Making via Sequential Majority Voting. In KR (Gerhard Brewka and Jérôme Lang, editors), pages 571-578, AAAI Press, 2008.
BibTeX:
@inproceedings{DBLP:conf/kr/PiniRVW08,
  author = {Pini, Maria Silvia and Rossi, Francesca and Venable, Kristen Brent and Walsh, Toby},
  title = {Dealing with Incomplete Agents' Preferences and an Uncertain Agenda in Group Decision Making via Sequential Majority Voting},
  booktitle = {KR},
  editor = {Brewka, Gerhard and Lang, J{\'{e}}r{\^{o}}me},
  publisher = {AAAI Press},
  year = {2008},
  pages = {571-578}
}
Maria Silvia Pini, Francesca Rossi, Kristen Brent Venable and Toby Walsh. Incompleteness and Incomparability in Preference Aggregation. In IJCAI (Manuela M. Veloso, editors), pages 1464-1469, 2007.
BibTeX:
@inproceedings{DBLP:conf/ijcai/PiniRVW07,
  author = {Pini, Maria Silvia and Rossi, Francesca and Venable, Kristen Brent and Walsh, Toby},
  title = {Incompleteness and Incomparability in Preference Aggregation},
  booktitle = {IJCAI},
  editor = {Veloso, Manuela M.},
  year = {2007},
  pages = {1464-1469},
  url = {http://dli.iiit.ac.in/ijcai/IJCAI-2007/PDF/IJCAI07-236.pdf}
}
John C. Platt, Nello Cristianini and John Shawe-Taylor. Large Margin DAGs for Multiclass Classification. In Advances in Neural Information Processing Systems 12 (NIPS-99) (Sara A. Solla, Todd K. Leen and Klaus-Robert Müller, editors), pages 547-553, MIT Press, 2000.
BibTeX:
@inproceedings{pairwiseDAGs,
  author = {Platt, John C. and Cristianini, Nello and Shawe-Taylor, John},
  title = {Large Margin DAGs for Multiclass Classification},
  booktitle = {Advances in Neural Information Processing Systems 12 (NIPS-99)},
  editor = {Solla, Sara A. and Leen, Todd K. and M{\"{u}}ller, Klaus-Robert},
  publisher = {MIT Press},
  year = {2000},
  pages = {547--553},
  url = {http://lara.enm.bris.ac.uk/cig/gzipped/2000/dagsvm.ps.gz}
}
David Price, Stefan Knerr, Léon Personnaz and Gérard Dreyfus. Pairwise Neural Network Classifiers with Probabilistic Outputs. In Advances in Neural Information Processing Systems 7 (NIPS-94) (Gerald Tesauro, D. Touretzky and Todd K. Leen, editors), Vol. 7, pages 1109-1116, MIT Press, 1994.
BibTeX:
@inproceedings{PairwiseNN-Probabilistic,
  author = {Price, David and Knerr, Stefan and Personnaz, L{\'{e}}on and Dreyfus, G{\'{e}}rard},
  title = {Pairwise Neural Network Classifiers with Probabilistic Outputs},
  booktitle = {Advances in Neural Information Processing Systems 7 (NIPS-94)},
  editor = {Tesauro, Gerald and Touretzky, D. and Leen, Todd K.},
  publisher = {MIT Press},
  year = {1994},
  volume = {7},
  pages = {1109--1116},
  url = {http://www.neurones.espci.fr/Articles_PS/knerr.pdf}
}
Filip Radlinski and Thorsten Joachims. Learning to Rank from Implicit Feedback. In Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD-05), pages 239-248, 2005.
BibTeX:
@inproceedings{Clickthrough-QueryChains,
  author = {Radlinski, Filip and Joachims, Thorsten},
  title = {Learning to Rank from Implicit Feedback},
  booktitle = {Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD-05)},
  year = {2005},
  pages = {239--248}
}
Filip Radlinski, Madhu Kurup and Thorsten Joachims. Evaluating Search Engine Relevance with Click-Based Metrics. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 337-361, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Radlinski,
  author = {Radlinski, Filip and Kurup, Madhu and Joachims, Thorsten},
  title = {Evaluating Search Engine Relevance with Click-Based Metrics},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {337--361},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Shyamsundar Rajaram and Shivani Agarwal. Generalization Bounds for $k$-Partite Ranking. In Proceedings of the NIPS 2005 Workshop on Learning to Rank (Shivani Agarwal, C. Cortes and R. Herbrich, editors), Whistler, BC, Canada, pages 28-23, 2005.
BibTeX:
@inproceedings{KPartiteRanking,
  author = {Rajaram, Shyamsundar and Agarwal, Shivani},
  title = {Generalization Bounds for $k$-Partite Ranking},
  booktitle = {Proceedings of the NIPS 2005 Workshop on Learning to Rank},
  editor = {Agarwal, Shivani and Cortes, C. and Herbrich, R.},
  year = {2005},
  pages = {28--23}
}
Doug Riecken. Perzonalized Views of Personalization. Communications of the ACM, Vol. 43(8), pages 26-29, 2000.
BibTeX:
@article{riec_pv00,
  author = {Riecken, Doug},
  title = {Perzonalized Views of Personalization},
  journal = {Communications of the ACM},
  year = {2000},
  volume = {43},
  number = {8},
  pages = {26--29}
}
Ryan Rifkin and Aldebaro Klautau. In Defense of One-Vs-All Classification. Journal of Machine Learning Research, Vol. 5, pages 101-141, 2004.
BibTeX:
@article{OneAgainstAll-Defense,
  author = {Rifkin, Ryan and Klautau, Aldebaro},
  title = {In Defense of One-Vs-All Classification},
  journal = {Journal of Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {101--141},
  url = {http://jmlr.csail.mit.edu/papers/v5/rifkin04a.html}
}
Francesca Rossi, Kristen Brent Venable and Toby Walsh. A Short Introduction to Preferences: Between Artificial Intelligence and Social Choice. , Morgan & Claypool Publishers, 2011.
BibTeX:
@book{Preferences-ShortIntro,
  author = {Rossi, Francesca and Venable, Kristen Brent and Walsh, Toby},
  title = {A Short Introduction to Preferences: Between Artificial Intelligence and Social Choice},
  publisher = {Morgan & Claypool Publishers},
  year = {2011}
}
Volker Roth and Koji Tsuda. Pairwise Coupling for Machine Recognition of Hand-Printed Japanese Characters. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-01), pages 1120-1125, IEEE Computer Society, 2001.
BibTeX:
@inproceedings{PairwiseCoupling-JapaneseCR,
  author = {Roth, Volker and Tsuda, Koji},
  title = {Pairwise Coupling for Machine Recognition of Hand-Printed Japanese Characters},
  booktitle = {Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR-01)},
  publisher = {IEEE Computer Society},
  year = {2001},
  pages = {1120--1125},
  url = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2001.990656}
}
Constantin A. Rothkopf and Christos Dimitrakakis. Preference Elicitation and Inverse Reinforcement Learning. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD-11), Part III (Dimitrios Gunopulos, Thomas Hofmann, Donato Malerba and Michalis Vazirgiannis, editors), Athens, Greece, Vol. 6913, pages 34-48, Springer, 2011.
BibTeX:
@inproceedings{InverseRL-PreferenceElicitation,
  author = {Rothkopf, Constantin A. and Dimitrakakis, Christos},
  title = {Preference Elicitation and Inverse Reinforcement Learning},
  booktitle = {Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD-11), Part III},
  editor = {Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato and Vazirgiannis, Michalis},
  publisher = {Springer},
  year = {2011},
  volume = {6913},
  pages = {34--48},
  doi = {http://dx.doi.org/10.1007/978-3-642-23808-6_3}
}
Thomas L. Saaty. Relative Measurement and its Generalization in Decision Making: Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors -- The Analytic Hierarchy/Network Process. Revista de la Real Academia de Ciencias Exactas, Fsicas y Naturales. Serie A: Matemáticas (RACSAM), Vol. 102(2), pages 251-318, 2008.
BibTeX:
@article{AnalyticHierarchyProcess-PairwiseComparisons,
  author = {Saaty, Thomas L.},
  title = {Relative Measurement and its Generalization in Decision Making: Why Pairwise Comparisons are Central in Mathematics for the Measurement of Intangible Factors -- The Analytic Hierarchy/Network Process},
  journal = {Revista de la Real Academia de Ciencias Exactas, Fsicas y Naturales. Serie A: Matemáticas (RACSAM)},
  year = {2008},
  volume = {102},
  number = {2},
  pages = {251--318},
  url = {http://www.rac.es/ficheros/doc/00576.PDF}
}
Thomas L. Saaty. Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World. , Pittsburgh, Pennsylvania, RWS Publications, 1999.
BibTeX:
@book{AnalyticHierarchyProcess,
  author = {Saaty, Thomas L.},
  title = {Decision Making for Leaders: The Analytic Hierarchy Process for Decisions in a Complex World},
  publisher = {RWS Publications},
  year = {1999}
}
Michael S. Schmidt. Identifying Speakers with Support Vector Networks. In Proceedings of the 28th Symposium on the Interface (INTERFACE-96), Sydney, Australia, 1996.
BibTeX:
@inproceedings{SVM-SpeakerIdentification-2,
  author = {Schmidt, Michael S.},
  title = {Identifying Speakers with Support Vector Networks},
  booktitle = {Proceedings of the 28th Symposium on the Interface (INTERFACE-96)},
  year = {1996},
  url = {http://www.stat.uga.edu/~lynne/symposium/paper1i3.ps.gz}
}
Michael S. Schmidt and Herbert Gish. Speaker Identification via Support Vector Classifiers. In Proceedings of the 21st IEEE International Conference Conference on Acoustics, Speech, and Signal Processing (ICASSP-96), Atlanta, GA, pages 105-108, 1996.
BibTeX:
@inproceedings{SVM-SpeakerIdentification,
  author = {Schmidt, Michael S. and Gish, Herbert},
  title = {Speaker Identification via Support Vector Classifiers},
  booktitle = {Proceedings of the 21st IEEE International Conference Conference on Acoustics, Speech, and Signal Processing (ICASSP-96)},
  year = {1996},
  pages = {105--108}
}
Jan Frederik Sima. Paarweise Hierarchische Klassifikation. School: TU Darmstadt, Knowledge Engineering Group., 2008.
BibTeX:
@mastersthesis{da:Sima,
  author = {Sima, Jan Frederik},
  title = {Paarweise Hierarchische Klassifikation},
  school = {TU Darmstadt, Knowledge Engineering Group},
  year = {2008},
  note = {Diplom},
  url = {http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/diplom/2008/Sima_Jan-Frederik.pdf}
}
Cees G. M. Snoek, Marcel Worring, Jan C. van Gemert, Jan-Mark Geusebroek and Arnold W. M. Smeulders. The Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia. In Proceedings of ACM Multimedia, Santa Barbara, CA, pages 421-430, 2006.
BibTeX:
@inproceedings{MediaMill-Challenge,
  author = {Snoek, Cees G. M. and Worring, Marcel and van Gemert, Jan C. and Geusebroek, Jan-Mark and Smeulders, Arnold W. M.},
  title = {The Challenge Problem for Automated Detection of 101 Semantic Concepts in Multimedia},
  booktitle = {Proceedings of ACM Multimedia},
  year = {2006},
  pages = {421--430}
}
Charles Spearman. The Proof and Measurement of Association Between Two Things. American Journal of Psychology, Vol. 15, pages 88-103, 1904.
BibTeX:
@article{Spearman,
  author = {Spearman, Charles},
  title = {The Proof and Measurement of Association Between Two Things},
  journal = {American Journal of Psychology},
  year = {1904},
  volume = {15},
  pages = {88--103}
}
Jan-Nikolas Sulzmann. Paarweiser Naive Bayes Klassifizierer. School: TU Darmstadt. September, , 2006.
BibTeX:
@mastersthesis{da:Sulzmann,
  author = {Sulzmann, Jan-Nikolas},
  title = {{Paarweiser Naive Bayes Klassifizierer}},
  school = {TU Darmstadt},
  year = {2006},
  note = {Diplom},
  url = {http://www.ke.informatik.tu-darmstadt.de/lehre/arbeiten/diplom/2006/Sulzmann_Jan-Nikolas.pdf}
}
Jan-Nikolas Sulzmann and Johannes Fürnkranz. Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier. In Proceedings of the 14th International Conference on Discovery Science (DS-11) (Tapio Elomaa, Jaakko Hollmén and Heikki Mannila, editors), Espoo, Finland, Vol. 6926, pages 323-334, Springer, 2011.
BibTeX:
@inproceedings{jf:DS-11,
  author = {Sulzmann, Jan-Nikolas and F{\"{u}}rnkranz, Johannes},
  title = {Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier},
  booktitle = {Proceedings of the 14th International Conference on Discovery Science (DS-11)},
  editor = {Elomaa, Tapio and Hollm{\'{e}}n, Jaakko and Mannila, Heikki},
  publisher = {Springer},
  year = {2011},
  volume = {6926},
  pages = {323--334},
  doi = {http://dx.doi.org/10.1007/978-3-642-24477-3_2}
}
T. J. Terpstra. The asymptotic normality and consistency of Kendall's test against trend, when ties are present in one ranking. Indagationes Mathematicae, Vol. 14, pages 327-333, 1952.
BibTeX:
@article{JonckheereTerpstra2,
  author = {Terpstra, T. J.},
  title = {The asymptotic normality and consistency of {Kendall}'s test against trend, when ties are present in one ranking},
  journal = {Indagationes Mathematicae},
  year = {1952},
  volume = {14},
  pages = {327--333}
}
Louis Leon Thurstone. A Law of Comparative Judgement. Psychological Review, Vol. 34, pages 278-286, 1927.
BibTeX:
@article{Thurstone,
  author = {Thurstone, Louis Leon},
  title = {A Law of Comparative Judgement},
  journal = {Psychological Review},
  year = {1927},
  volume = {34},
  pages = {278--286},
  url = {http://www.brocku.ca/MeadProject/Thurstone/Thurstone_1929a.html}
}
Nicolaus Tideman. Collective Decisions and Voting: The Potential for Public Choice. , Ashgate Publishing, 2006.
BibTeX:
@book{CollectiveDecisionsAndVoting,
  author = {Tideman, Nicolaus},
  title = {Collective Decisions and Voting: The Potential for Public Choice},
  publisher = {Ashgate Publishing},
  year = {2006}
}
Lus Torgo and João Gama. Regression Using Classification Algorithms. Intelligent Data Analysis, Vol. 1(4), 1997.
BibTeX:
@article{RegressionByClassification,
  author = {Torgo, Lu{\'{\i}}s and Gama, Jo{\~{a}}o},
  title = {Regression Using Classification Algorithms},
  journal = {Intelligent Data Analysis},
  year = {1997},
  volume = {1},
  number = {4}
}
Vincenc Torra. Learning Aggregation Operators for Preference Modeling. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 317-333, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Torra,
  author = {Torra, Vincen{\c c}},
  title = {Learning Aggregation Operators for Preference Modeling},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {317--333},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Craig A. Tovey. Hill Climbing with Multiple Local Optima. SIAM Journal on Algebraic and Discrete Methods, Vol. 6(3), pages 384-393, SIAM, 1985.
BibTeX:
@article{tovey:384,
  author = {Tovey, Craig A.},
  title = {Hill Climbing with Multiple Local Optima},
  journal = {SIAM Journal on Algebraic and Discrete Methods},
  publisher = {SIAM},
  year = {1985},
  volume = {6},
  number = {3},
  pages = {384-393},
  url = {http://link.aip.org/link/?SML/6/384/1},
  doi = {http://dx.doi.org/10.1137/0606040}
}
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski and Tom Heskes. Co-Regularized Least Squares for Label Ranking. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 107-123, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Tsivtsivadze,
  author = {Tsivtsivadze, Evgeni and Pahikkala, Tapio and Boberg, Jorma and Salakoski, Tapio and Heskes, Tom},
  title = {Co-Regularized Least Squares for Label Ranking},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {107--123},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Grigorios Tsoumakas and Ioannis Katakis. Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining, Vol. 3(3), pages 1-17, 2007.
BibTeX:
@article{MultiLabel-Overview,
  author = {Tsoumakas, Grigorios and Katakis, Ioannis},
  title = {Multi-Label Classification: An Overview},
  journal = {International Journal of Data Warehousing and Mining},
  year = {2007},
  volume = {3},
  number = {3},
  pages = {1--17},
  url = {http://lpis.csd.auth.gr/publications/tsoumakas-ijdwm.pdf}
}
Antti Ukkonen, Kai Puolamäki, Aristides Gionis and Heikki Mannila. A Randomized Approximation Algorithm for Computing Bucket Orders. Information Processing Letters, Vol. 109, pages 356-359, 2009.
BibTeX:
@article{BucketOrders-2,
  author = {Ukkonen, Antti and Puolam{\"{a}}ki, Kai and Gionis, Aristides and Mannila, Heikki},
  title = {A Randomized Approximation Algorithm for Computing Bucket Orders},
  journal = {Information Processing Letters},
  year = {2009},
  volume = {109},
  pages = {356--359}
}
Shankar Vembu and Thomas Gärtner. Label Ranking Algorithms: A Survey. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 45-64, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Vembu,
  author = {Vembu, Shankar and G{\"{a}}rtner, Thomas},
  title = {Label Ranking Algorithms: A Survey},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {45--64},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Celine Vens, Jan Struyf, Leander Schietgat, Sav so Dv zeroski and Hendrik Blockeel. Decision Trees for Hierarchical Multi-Label Classification. Machine Learning, Vol. 73(2), pages 185-214, 2008.
BibTeX:
@article{HierarchicalMultilabel,
  author = {Vens, Celine and Struyf, Jan and Schietgat, Leander and D{\v z}eroski, Sa{\v s}o and Blockeel, Hendrik},
  title = {Decision Trees for Hierarchical Multi-Label Classification},
  journal = {Machine Learning},
  year = {2008},
  volume = {73},
  number = {2},
  pages = {185--214},
  url = {https://lirias.kuleuven.be/bitstream/123456789/186698/4/hmc.pdf}
}
Willem Waegeman and Bernard De Baets. A Survey on ROC-based Ordinal Regression. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 127-154, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Waegeman,
  author = {Waegeman, Willem and De Baets, Bernard},
  title = {A Survey on {ROC}-based Ordinal Regression},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {127--154},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Willem Waegeman, Bernard De Baets and Luc Boullart. A Graph-theoretic Approach for Reducing One-versus-one Multi-class Classification to Ranking. In Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG-08) (S. Kaski, S. V. N. Vishwanathan and Stefan Wrobel, editors), Helsinki, Finland, 2008.
BibTeX:
@inproceedings{ReducingPairwiseToRanking,
  author = {Waegeman, Willem and De Baets, Bernard and Boullart, Luc},
  title = {A Graph-theoretic Approach for Reducing One-versus-one Multi-class Classification to Ranking},
  booktitle = {Proceedings of the 6th International Workshop on Mining and Learning with Graphs (MLG-08)},
  editor = {Kaski, S. and Vishwanathan, S. V. N. and Wrobel, Stefan},
  year = {2008}
}
Jun Wang. Artificial Neural Networks versus Natural Neural Networks: A Connectionist Paradigm for Preference Assessment. Decision Support Systems, Vol. 11, pages 415-429, 1994.
BibTeX:
@article{wang_an94,
  author = {Wang, Jun},
  title = {Artificial Neural Networks versus Natural Neural Networks: {A} Connectionist Paradigm for Preference Assessment},
  journal = {Decision Support Systems},
  year = {1994},
  volume = {11},
  pages = {415--429}
}
Christian Wirth and Johannes Fürnkranz. Preference Learning from Annotated Game Databases. In Proceedings of the 16th LWA Workshops: KDML, IR and FGWM (Thomas Seidl, Marwan Hassani and Christian Beecks, editors), Aachen, Germany September, , Vol. 1226, pages 57-68, CEUR-WS.org, 2014.
BibTeX:
@inproceedings{jf:LWA-14,
  author = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
  title = {Preference Learning from Annotated Game Databases},
  booktitle = {Proceedings of the 16th LWA Workshops: KDML, IR and FGWM},
  editor = {Seidl, Thomas and Hassani, Marwan and Beecks, Christian},
  publisher = {CEUR-WS.org},
  year = {2014},
  volume = {1226},
  pages = {57--68},
  url = {http://ceur-ws.org/Vol-1226/paper11.pdf}
}
Christian Wirth and Johannes Fürnkranz. A Policy Iteration Algorithm for Learning from Preference-based Feedback. In Advances in Intelligent Data Analysis XII: 12th International Symposium (IDA-13) (Allan Tucker, Frank Höppner, Arno Siebes and Stephen Swift, editors) October, , Vol. 8207, pages 427-437, Springer-Verlag, 2013.
BibTeX:
@inproceedings{cwIDA13,
  author = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
  title = {A Policy Iteration Algorithm for Learning from Preference-based Feedback},
  booktitle = {Advances in Intelligent Data Analysis XII: 12th International Symposium (IDA-13)},
  editor = {Tucker, Allan and H{\"{o}}ppner, Frank and Siebes, Arno and Swift, Stephen},
  publisher = {Springer-Verlag},
  year = {2013},
  volume = {8207},
  pages = {427--437}
}
Christian Wirth and Johannes Fürnkranz. EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning. In Proceedings of the 5th Asian Conference on Machine Learning, (ACML-13), Vol. 29, pages 483-497, JMLR.org, 2013.
BibTeX:
@inproceedings{jf:ACML-13,
  author = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
  title = {{EPMC}: {Every} Visit Preference {Monte Carlo} for Reinforcement Learning},
  booktitle = {Proceedings of the 5th Asian Conference on Machine Learning, (ACML-13)},
  publisher = {JMLR.org},
  year = {2013},
  volume = {29},
  pages = {483--497},
  url = {http://jmlr.org/proceedings/papers/v29/Wirth13.html}
}
Christian Wirth and Johannes Fürnkranz. Preference-Based Reinforcement Learning: A Preliminary Survey. In Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning from Generalized Feedback: Beyond Numeric Rewards, 2013.
BibTeX:
@inproceedings{jf:PBRL-13-Survey,
  author = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
  title = {Preference-Based Reinforcement Learning: A Preliminary Survey},
  booktitle = {Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning from Generalized Feedback: Beyond Numeric Rewards},
  year = {2013},
  url = {http://www.ke.tu-darmstadt.de/events/PBRL-13/papers/10-Wirth.pdf}
}
Christian Wirth and Johannes Fürnkranz. Learning from Trajectory-Based Action Preferences. In Proceedings of the ICRA 2013 Workshop on Autonomous Learning May, , 2013.
BibTeX:
@inproceedings{TrajBasedActionPrefs,
  author = {Wirth, Christian and F{\"{u}}rnkranz, Johannes},
  title = {Learning from Trajectory-Based Action Preferences},
  booktitle = {Proceedings of the ICRA 2013 Workshop on Autonomous Learning},
  year = {2013},
  url = {http://ipvs.informatik.uni-stuttgart.de/mlr/spp-wordpress/wp-content/uploads/wirth2013.pdf}
}
Moritz Wissenbach. Object Feature Coding: A Decomposition Framework Unifying Object and Label Ranking. School: Knowledge Engineering Group, TU Darmstadt. October, , 2010.
BibTeX:
@mastersthesis{da:wissenbach,
  author = {Wissenbach, Moritz},
  title = {Object Feature Coding: A Decomposition Framework Unifying Object and Label Ranking},
  school = {Knowledge Engineering Group, TU Darmstadt},
  year = {2010},
  url = {http://www.ke.tu-darmstadt.de/lehre/arbeiten/diplom/2010/Wissenbach_Moritz.pdf}
}
Qiang Wu, Christopher J. C. Burges, Krysta Marie Svore and Jianfeng Gao. Adapting boosting for information retrieval measures. Inf. Retr., Vol. 13(3), pages 254-270, 2010.
BibTeX:
@article{DBLP:journals/ir/WuBSG10,
  author = {Wu, Qiang and Burges, Christopher J. C. and Svore, Krysta Marie and Gao, Jianfeng},
  title = {Adapting boosting for information retrieval measures},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {254-270},
  doi = {http://dx.doi.org/10.1007/s10791-009-9112-1}
}
Ting-Fan Wu, Chih-Jen Lin and Ruby C. Weng. Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research, Vol. 5, pages 975-1005, 2004.
BibTeX:
@article{PairwiseCoupling-Estimates,
  author = {Wu, Ting-Fan and Lin, Chih-Jen and Weng, Ruby C.},
  title = {Probability Estimates for Multi-class Classification by Pairwise Coupling},
  journal = {Journal of Machine Learning Research},
  year = {2004},
  volume = {5},
  pages = {975--1005},
  url = {http://www.jmlr.org/papers/volume5/wu04a/wu04a.pdf}
}
Fusun Yaman, Thomas J. Walsh, Michael L. Littman and Marie desJardins. Learning Lexicographic Preference Models. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 251-272, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Yaman,
  author = {Yaman, Fusun and Walsh, Thomas J. and Littman, Michael L. and desJardins, Marie},
  title = {Learning Lexicographic Preference Models},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {251--272},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Emine Yilmaz and Stephen Robertson. On the choice of effectiveness measures for learning to rank. Inf. Retr., Vol. 13(3), pages 271-290, 2010.
BibTeX:
@article{DBLP:journals/ir/YilmazR10,
  author = {Yilmaz, Emine and Robertson, Stephen},
  title = {On the choice of effectiveness measures for learning to rank},
  journal = {Inf. Retr.},
  year = {2010},
  volume = {13},
  number = {3},
  pages = {271-290},
  doi = {http://dx.doi.org/10.1007/s10791-009-9116-x}
}
Philip L. H. Yu, W. M. Wan and Paul H. Lee. Decision Tree Modeling for Ranking Data. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 83-106, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Yu,
  author = {Yu, Philip L. H. and Wan, W. M. and Lee, Paul H.},
  title = {Decision Tree Modeling for Ranking Data},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {83--106},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Jianping Zhang, Jerzy W. Bala, Ali Hadjarian and Brent Han. Ranking Cases with Classification Rules. In Preference Learning (Johannes Fürnkranz and Eyke Hüllermeier, editors), pages 155-177, Springer-Verlag, 2010.
BibTeX:
@incollection{plbook:Zhang,
  author = {Zhang, Jianping and Bala, Jerzy W. and Hadjarian, Ali and Han, Brent},
  title = {Ranking Cases with Classification Rules},
  booktitle = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  pages = {155--177},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16-19, 2008. In KR (Gerhard Brewka and Jérôme Lang, editors), AAAI Press, 2008.
BibTeX:
@proceedings{DBLP:conf/kr/2008,,
  title = {Principles of Knowledge Representation and Reasoning: Proceedings of the Eleventh International Conference, KR 2008, Sydney, Australia, September 16-19, 2008},
  booktitle = {KR},
  editor = {Brewka, Gerhard and Lang, J{\'{e}}r{\^{o}}me},
  publisher = {AAAI Press},
  year = {2008}
}
Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards. (Johannes Fürnkranz and Eyke Hüllermeier, editors), 2013.
BibTeX:
@proceedings{jf:PBRL-13,,
  title = {Proceedings of the ECML/PKDD-13 Workshop on Reinforcement Learning with Generalized Feedback: Beyond Numeric Rewards},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  year = {2013},
  url = {http://www.ke.tu-darmstadt.de/events/PBRL-13/}
}
Proceedings of the ECAI-12 Workshop on Preference Learning: Problems and Applications in AI (PL-12). (Johannes Fürnkranz and Eyke Hüllermeier, editors), 2012.
BibTeX:
@proceedings{jf:PL-12,,
  title = {Proceedings of the ECAI-12 Workshop on Preference Learning: Problems and Applications in AI (PL-12)},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  year = {2012},
  url = {http://www.ke.tu-darmstadt.de/events/PL-12/PL12-Proceedings.pdf}
}
Preference Learning. (Johannes Fürnkranz and Eyke Hüllermeier, editors), Springer-Verlag, 2010.
BibTeX:
@book{plbook,,
  title = {Preference Learning},
  editor = {F{\"{u}}rnkranz, Johannes and H{\"{u}}llermeier, Eyke},
  publisher = {Springer-Verlag},
  year = {2010},
  url = {http://www.springer.com/978-3-642-14124-9},
  doi = {http://dx.doi.org/10.1007/978-3-642-14125-6}
}
Special Issue on Preference Learning and Ranking. Machine Learning (Eyke Hüllermeier and Johannes Fürnkranz, editors), Vol. 93(2-3), 2013.
BibTeX:
@article{jf:MLJ-SI-Preferences,,
  title = {Special Issue on Preference Learning and Ranking},
  journal = {Machine Learning},
  editor = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  year = {2013},
  volume = {93},
  number = {2-3}
}
Proceedings of the ECML/PKDD-10 Workshop and Tutorial on Preference Learning. (Eyke Hüllermeier and Johannes Fürnkranz, editors), Barcelona, Spain, 2010.
BibTeX:
@proceedings{jf:PL-10-WS,,
  title = {Proceedings of the ECML/PKDD-10 Workshop and Tutorial on Preference Learning},
  editor = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes},
  year = {2010},
  url = {http://www.ke.tu-darmstadt.de/events/PL-10/}
}
IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007. In IJCAI (Manuela M. Veloso, editors), 2007.
BibTeX:
@proceedings{DBLP:conf/ijcai/2007,,
  title = {IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007},
  booktitle = {IJCAI},
  editor = {Veloso, Manuela M.},
  year = {2007}
}
Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010. In MLD'10 (Min-Ling Zhang, Grigorios Tsoumakas and Zhi-Hua Zhou, editors) June, , 2010.
BibTeX:
@proceedings{zhang10mlworkshop,,
  title = {Working Notes of the 2nd International Workshop on Learning from Multi-Label Data at ICML/COLT 2010},
  booktitle = {MLD'10},
  editor = {Zhang, Min-Ling and Tsoumakas, Grigorios and Zhou, Zhi-Hua},
  year = {2010},
  url = {http://cse.seu.edu.cn/conf/MLD10/files/MLD'10.pdf}
}
New results on error correcting output codes of kernel machines. Neural Networks, IEEE Transactions on, Vol. 15(1), pages 45-54, 2004.
BibTeX:
@article{1263577,,
  title = {New results on error correcting output codes of kernel machines},
  journal = {Neural Networks, IEEE Transactions on},
  year = {2004},
  volume = {15},
  number = {1},
  pages = {45--54},
  doi = {http://dx.doi.org/10.1109/tnn.2003.820841}
}

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