Overview
- Editors:
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Johannes Fürnkranz
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FB Informatik, TU Darmstadt, Darmstadt, Germany
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Eyke Hüllermeier
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FB Mathematik und Informatik, Philipps-Universität Marburg, Marburg, Germany
- This is the first book dedicated to this topic
- This topic has attracted considerable attention in artificial intelligence research in recent years
- A comprehensive treatment
- Includes supplementary material: sn.pub/extras
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Table of contents (20 chapters)
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- Johannes Fürnkranz, Eyke Hüllermeier
Pages 1-17
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- Fabio Aiolli, Alessandro Sperduti
Pages 19-42
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Label Ranking
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- Shankar Vembu, Thomas Gärtner
Pages 45-64
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- Johannes Fürnkranz, Eyke Hüllermeier
Pages 65-82
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- Philip L. H. Yu, Wai Ming Wan, Paul H. Lee
Pages 83-106
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- Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes
Pages 107-123
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Instance Ranking
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Front Matter
Pages 125-125
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- Willem Waegeman, Bernard De Baets
Pages 127-154
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- Jianping Zhang, Jerzy W. Bala, Ali Hadjarian, Brent Han
Pages 155-177
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Object Ranking
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Front Matter
Pages 179-179
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- Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho
Pages 181-201
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- Toshihiro Kamishima, Shotaro Akaho
Pages 203-215
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- Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński, Marcin Szeląg
Pages 217-247
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Preferences in Multi-Attribute Domains
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Front Matter
Pages 249-249
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- Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
Pages 251-272
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- Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini
Pages 273-296
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- Joachim Giesen, Klaus Mueller, Bilyana Taneva, Peter Zolliker
Pages 297-315
About this book
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 previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. 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. And, generalizing beyond training data, models thus learned may be used for preference prediction.
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 first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems.
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.
Reviews
From the 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)
Editors and Affiliations
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FB Informatik, TU Darmstadt, Darmstadt, Germany
Johannes Fürnkranz
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FB Mathematik und Informatik, Philipps-Universität Marburg, Marburg, Germany
Eyke Hüllermeier