Fürnkranz, Johannes
Preference Learning
1. Preference Learning: An Introduction
Johannes Fürnkranz, Eyke Hüllermeier
2. A Preference Optimization Based Unifying Framework for Supervised Learning Problems
Fabio Aiolli, Alessandro Sperduti
3. Label Ranking Algorithms: A Survey
Shankar Vembu, Thomas Gärtner
4. Preference Learning and Ranking by Pairwise Comparison
Johannes Fürnkranz, Eyke Hüllermeier
5. Decision Tree Modeling for Ranking Data
Philip L. H. Yu, Wai Ming Wan, Paul H. Lee
6. Co-Regularized Least-Squares for Label Ranking
Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes
7. A Survey on ROC-based Ordinal Regression
Willem Waegeman, Bernard De Baets
8. Ranking Cases with Classification Rules
Jianping Zhang, Jerzy W. Bala, Ali Hadjarian, Brent Han
9. A Survey and Empirical Comparison of Object Ranking Methods
Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho
10. Dimension Reduction for Object Ranking
Toshihiro Kamishima, Shotaro Akaho
11. Learning of Rule Ensembles for Multiple Attribute Ranking Problems
Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński, Marcin Szeląg
12. Learning Lexicographic Preference Models
Fusun Yaman, Thomas J. Walsh, Michael L. Littman, Marie desJardins
13. Learning Ordinal Preferences on Multiattribute Domains: The Case of CP-nets
Yann Chevaleyre, Frédéric Koriche, Jérôme Lang, Jérôme Mengin, Bruno Zanuttini
14. Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models
Joachim Giesen, Klaus Mueller, Bilyana Taneva, Peter Zolliker
15. Learning Aggregation Operators for Preference Modeling
Vicenç Torra
16. Evaluating Search Engine Relevance with Click-Based Metrics
Filip Radlinski, Madhu Kurup, Thorsten Joachims
17. Learning SVM Ranking Functions from User Feedback Using Document Metadata and Active Learning in the Biomedical Domain
Robert Arens
18. Learning Preference Models in Recommender Systems
Marco de Gemmis, Leo Iaquinta, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, Giovanni Semeraro
19. Collaborative Preference Learning
Alexandros Karatzoglou, Markus Weimer
20. Discerning Relevant Model Features in a Content-based Collaborative Recommender System
Alejandro Bellogín, Iván Cantador, Pablo Castells, Álvaro Ortigosa
Nyckelord: Computer Science, Artificial Intelligence (incl. Robotics), Data Mining and Knowledge Discovery
- Författare
- Fürnkranz, Johannes
- Hüllermeier, Eyke
- Utgivare
- Springer
- Utgivningsår
- 2011
- Språk
- en
- Utgåva
- 1
- Sidantal
- 9 sidor
- Kategori
- Datateknik, Datakommunikation
- Format
- E-bok
- eISBN (PDF)
- 9783642141256