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Preference Learning
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 declarati...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2011.
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| Edición: | 1st ed. 2011. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Preference Learning: An Introduction
- A Preference Optimization Based Unifying Framework for Supervised Learning Problems
- Label Ranking Algorithms: A Survey
- Preference Learning and Ranking by Pairwise Comparison
- Decision Tree Modeling for Ranking Data
- Co-regularized Least-Squares for Label Ranking
- A Survey on ROC-Based Ordinal Regression
- Ranking Cases with Classification Rules
- A Survey and Empirical Comparison of Object Ranking Methods
- Dimension Reduction for Object Ranking
- Learning of Rule Ensembles for Multiple Attribute Ranking Problems
- Learning Lexicographic Preference Models
- Learning Ordinal Preferences on Multiattribute Domains: the Case of CP-nets
- Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models
- Learning Aggregation Operators for Preference Modeling
- Evaluating Search Engine Relevance with Click-Based Metrics
- Learning SVM Ranking Function from User Feedback Using Document
- Metadata and Active Learning in the Biomedical Domain
- Learning Preference Models in Recommender Systems
- Collaborative Preference Learning
- Discerning Relevant Model Features in a Content-Based Collaborative Recommender System
- Author Index
- Subject Index.