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Item Details
Title: PREFERENCE LEARNING
By: Johannes Furnkranz (Editor), Eyke Hullermeier (Editor)
Format: Hardback

List price: £119.99


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 3642141242
ISBN 13: 9783642141249
Publisher: SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG
Pub. date: 10 October, 2010
Edition: 2011 ed.
Pages: 466
Description: The first book dedicated to this new branch of machine learning and data mining, this comprehensive treatment, which covers everything from label ranking to preference learning and recommender systems, will be required reading for researchers working in AI.
Synopsis: 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.
Illustrations: IX, 466 p.
Publication: Germany
Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Returns: Returnable
Some other items by this author:
CASE-BASED APPROXIMATE REASONING (PB)
CASE-BASED REASONING RESEARCH AND DEVELOPMENT (PB)
COMPUTATIONAL INTELLIGENCE FOR KNOWLEDGE-BASED SYSTEM DESIGN (PB)
DISCOVERY SCIENCE (PB)
FOUNDATIONS OF RULE LEARNING (HB)
FOUNDATIONS OF RULE LEARNING (PB)
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS (PB)
INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS (PB)
KI 2017: ADVANCES IN ARTIFICIAL INTELLIGENCE (PB)
KNOWLEDGE DISCOVERY IN DATABASES PKDD 2006 (PB)
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES (PB)
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES (PB)
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES (PB)
MACHINE LEARNING, ECML 2006 (PB)
MACHINES THAT LEARN TO PLAY GAMES (HB)
PREFERENCE LEARNING (PB)
SCALABLE UNCERTAINTY MANAGEMENT (PB)

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