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* Front Matter. http://link.springer.com/content/pdf/bfm%3A978-3-642-14125-6%2F1%2F1.pdf
 
* Front Matter. http://link.springer.com/content/pdf/bfm%3A978-3-642-14125-6%2F1%2F1.pdf
  
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* 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
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* Johannes Fürnkranz, Eyke Hüllermeier: [[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
  
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* 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
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* Fabio Aiolli, Alessandro Sperduti: [[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'''
 
'''Label Ranking'''
  
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* 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
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* Shankar Vembu, Thomas Gärtner: [[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
  
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* 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  
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* Johannes Fürnkranz, Eyke Hüllermeier: [[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  
  
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    Decision Tree Modeling for Ranking Data
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*  Philip L. H. Yu, Wai Ming Wan, Paul H. Lee: [[Decision Tree Modeling for Ranking Data]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 83-106. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_5
  
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    Yu, Philip L. H. (et al.)
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*  Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes: [[Co-Regularized Least-Squares for Label Ranking]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 107-123. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_6
  
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    Seiten 83-106
 
  
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    Co-Regularized Least-Squares for Label Ranking
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'''Instance Ranking'''
  
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    Tsivtsivadze, Evgeni (et al.)
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* Front Matter: http://link.springer.com/content/pdf/bfm%3A978-3-642-14125-6%2F3%2F1.pdf
  
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    Seiten 107-123
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*  Willem Waegeman, Bernard De Baets: [[A Survey on ROC-based Ordinal Regression]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 127-154. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_7
  
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    A Survey on ROC-based Ordinal Regression
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*  Jianping Zhang, Jerzy W. Bala, Ali Hadjarian, Brent Han: [[Ranking Cases with Classification Rules]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 155-177. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_8
  
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    Waegeman, Willem (et al.)
 
  
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    Seiten 127-154
 
  
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    Ranking Cases with Classification Rules
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'''Object Ranking'''
  
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    Zhang, Jianping (et al.)
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* Front Matter: http://link.springer.com/content/pdf/bfm%3A978-3-642-14125-6%2F4%2F1.pdf
  
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    Seiten 155-177
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*  Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho: [[A Survey and Empirical Comparison of Object Ranking Methods]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 181-201. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_9
  
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    A Survey and Empirical Comparison of Object Ranking Methods
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*  Toshihiro Kamishima, Shotaro Akaho: [[Dimension Reduction for Object Ranking]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 203-215. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_10
  
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    Kamishima, Toshihiro (et al.)
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*  Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński, Marcin Szeląg: [[Learning of Rule Ensembles for Multiple Attribute Ranking Problems]]. In: Fürnkranz, J. and Hüllermeier, E.: [[Preference Learning]], 2011, 217-247. http://link.springer.com/chapter/10.1007/978-3-642-14125-6_11
  
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    Seiten 181-201
 
  
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    Dimension Reduction for Object Ranking
 
  
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    Kamishima, Toshihiro (et al.)
 
  
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    Seiten 203-215
 
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    Learning of Rule Ensembles for Multiple Attribute Ranking Problems
 
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    Dembczyński, Krzysztof (et al.)
 
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     Learning Lexicographic Preference Models
 
     Learning Lexicographic Preference Models

Version vom 27. November 2015, 21:23 Uhr

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)


Label Ranking


Instance Ranking


Object Ranking 



   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


Links

Full Text

intern file

Sonstige Links

http://link.springer.com/book/10.1007/978-3-642-14125-6

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