Preference learning
著者
書誌事項
Preference learning
Springer, c2010
大学図書館所蔵 全5件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references and indexes
内容説明・目次
内容説明
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.
目次
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
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