Preference Learning
Inhaltsverzeichnis
Reference
Johannes Fürnkranz, Eyke Hüllermeier (eds.): Preference Learning. Springer Berlin Heidelberg, 2011. ISBN: 978-3-642-14124-9 (Print) 978-3-642-14125-6 (Online)
DOI
http://dx.doi.org/10.1007/978-3-642-14125-6
Abstract
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in recent years. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. Preference learning is concerned with the acquisition of preference models from data – it involves learning from observations that reveal information about the preferences of an individual or a class of individuals, and building models that generalize beyond such training data. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The remainder of the book is organized into parts that follow the developed framework, complementing survey articles with in-depth treatises of current research topics in this area. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
Extended Abstract
Reviews
"The book looks at three major types of preference learning: label ranking, instance ranking, and object ranking. … chapters contain case studies and actual experiments to illustrate the claims made within. … this is a useful book in an emerging and important area, and hence would be of interest to machine learning researchers. The book is quite readable to that audience, despite a heavy emphasis on formal treatment." M. Sasikumar, ACM Computing Reviews, September, 2011
Bibtex
@book{ year={2011}, isbn={978-3-642-14124-9 (Print), 978-3-642-14125-6 (Online)}, booktitle={Preference Learning}, editor={Fürnkranz, Johannes and Hüllermeier, Eyke}, doi={10.1007/978-3-642-14125-6}, url={http://dx.doi.org/10.1007/978-3-642-14125-6, http://de.evo-art.org/index.php?title=Computers_and_Creativity }, publisher={Springer Berlin Heidelberg}, language={English} }
Table of contents (20 chapters)
- Fürnkranz, Johannes (et al.): Preference Learning: An Introduction. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 1-17. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_1
- Aiolli, Fabio (et al.): A Preference Optimization Based Unifying Framework for Supervised Learning Problems. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 19-42. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_2
Label Ranking
- Vembu, Shankar (et al.): Label Ranking Algorithms: A Survey. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 45-64. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_3
- Fürnkranz, Johannes (et al.): Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 65-82. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_4
Decision Tree Modeling for Ranking Data
Yu, Philip L. H. (et al.)
Seiten 83-106
Co-Regularized Least-Squares for Label Ranking
Tsivtsivadze, Evgeni (et al.)
Seiten 107-123
A Survey on ROC-based Ordinal Regression
Waegeman, Willem (et al.)
Seiten 127-154
Ranking Cases with Classification Rules
Zhang, Jianping (et al.)
Seiten 155-177
A Survey and Empirical Comparison of Object Ranking Methods
Kamishima, Toshihiro (et al.)
Seiten 181-201
Dimension Reduction for Object Ranking
Kamishima, Toshihiro (et al.)
Seiten 203-215
Learning of Rule Ensembles for Multiple Attribute Ranking Problems
Dembczyński, Krzysztof (et al.)
Seiten 217-247
Learning Lexicographic Preference Models
Yaman, Fusun (et al.)
Seiten 251-272
Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets
Chevaleyre, Yann (et al.)
Seiten 273-296
Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models
Giesen, Joachim (et al.)
Seiten 297-315
Learning Aggregation Operators for Preference Modeling
Torra, Vicenç
Seiten 317-333
Evaluating Search Engine Relevance with Click-Based Metrics
Radlinski, Filip (et al.)
Seiten 337-361
Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain
Arens, Robert
Seiten 363-383
Learning Preference Models in Recommender Systems
Gemmis, Marco de (et al.)
Seiten 387-407
Collaborative Preference Learning
Karatzoglou, Alexandros (et al.)
Seiten 409-427
Discerning Relevant Model Features in a Content-based Collaborative Recommender System
Bellogín, Alejandro (et al.)
Seiten 429-455