ISBN: 9783642141256
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… Plus…
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ISBN: 9783642141256
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 involve… Plus…
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ISBN: 9783642141256
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ISBN: 9783642141256
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… Plus…
ISBN: 9783642141256
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 involve… Plus…
ISBN: 9783642141256
Preference Learning: ab 160.49 € eBooks > Sachthemen & Ratgeber > Computer & Internet Springer-Verlag GmbH, Springer-Verlag GmbH
2010, ISBN: 9783642141256
eBooks, eBook Download (PDF), 2011, [PU: Springer Berlin], Springer Berlin, 2010
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Informations détaillées sur le livre - Preference Learning
EAN (ISBN-13): 9783642141256
Date de parution: 2010
Editeur: Springer Berlin
454 Pages
Langue: eng/Englisch
Livre dans la base de données depuis 2011-02-02T11:35:24+01:00 (Zurich)
Page de détail modifiée en dernier sur 2022-05-06T16:04:43+02:00 (Zurich)
ISBN/EAN: 9783642141256
ISBN - Autres types d'écriture:
978-3-642-14125-6
Autres types d'écriture et termes associés:
Auteur du livre: hülle, hull, eyk, hüller, hulle, schroeder, fürnkranz
Titre du livre: préférence, preference
Données de l'éditeur
Auteur: Johannes Fürnkranz; Eyke Hüllermeier
Titre: Preference Learning
Editeur: Springer; Springer Berlin
466 Pages
Date de parution: 2010-11-19
Berlin; Heidelberg; DE
Langue: Anglais
160,49 € (DE)
165,00 € (AT)
189,00 CHF (CH)
Available
IX, 466 p.
EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Artificial intelligence; Data mining; Information retrieval; Instance ranking; Label ranking; Learning; Machine learning; Multicriteria decision-making; Object ranking; Operations research; Preference learning; Preference prediction; Reasoning; Recommender systems; Supevised learning; B; Artificial Intelligence; Data Mining and Knowledge Discovery; Computer Science; Data Mining; Wissensbasierte Systeme, Expertensysteme; BC
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.This is the first book dedicated to this topic This topic has attracted considerable attention in artificial intelligence research in recent years A comprehensive treatment Includes supplementary material: sn.pub/extras
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