Editors:
- 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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Front Matter
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Label Ranking
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Front Matter
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Instance Ranking
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Front Matter
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Preferences in Multi-Attribute Domains
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Front Matter
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About this book
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
Bibliographic Information
Book Title: Preference Learning
Editors: Johannes Fürnkranz, Eyke Hüllermeier
DOI: https://doi.org/10.1007/978-3-642-14125-6
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2011
Hardcover ISBN: 978-3-642-14124-9Published: 10 October 2010
Softcover ISBN: 978-3-642-42230-0Published: 28 September 2014
eBook ISBN: 978-3-642-14125-6Published: 19 November 2010
Edition Number: 1
Number of Pages: IX, 466
Topics: Artificial Intelligence, Data Mining and Knowledge Discovery