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Preference Learning / Eyke Hüllermeier (u. a.) / Buch / HC runder Rücken kaschiert / ix / Englisch / 2010 / Springer Berlin / EAN 9783642141249 - edition reliée, livre de poche

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Preference Learning | Eyke Hüllermeier (u. a.) | Buch | HC runder Rücken kaschiert | ix | Englisch | 2010 | Springer Berlin | EAN 9783642141249 - edition reliée, livre de poche

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Preference Learning - edition reliée, livre de poche

2010, ISBN: 9783642141249

Editor: Fürnkranz, Johannes, Editor: Hüllermeier, Eyke, Springer, Hardcover, Auflage: 2011, 475 Seiten, Publiziert: 2010-10-10T00:00:01Z, Produktgruppe: Book, Hersteller-Nr.: 76882, 0.96 … Plus…

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Preference Learning - edition reliée, livre de poche

2010, ISBN: 9783642141249

Editor: Fürnkranz, Johannes, Editor: Hüllermeier, Eyke, Springer, Hardcover, Auflage: 2011, 475 Seiten, Publiziert: 2010-10-10T00:00:01Z, Produktgruppe: Book, Hersteller-Nr.: 76882, 0.96 … Plus…

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Détails sur le livre
Preference Learning
Auteur:
Titre:
ISBN:

3642141242


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.

Informations détaillées sur le livre - Preference Learning


EAN (ISBN-13): 9783642141249
ISBN (ISBN-10): 3642141242
Version reliée
Livre de poche
Date de parution: 2010
Editeur: Springer
466 Pages
Poids: 0,915 kg
Langue: eng/Englisch

Livre dans la base de données depuis 2009-04-13T04:35:17+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-01-22T14:19:20+01:00 (Zurich)
ISBN/EAN: 3642141242

ISBN - Autres types d'écriture:
3-642-14124-2, 978-3-642-14124-9
Autres types d'écriture et termes associés:
Auteur du livre: hülle, hull, eyk, twain mark, hüller, hulle, johannes uhl, fürnkranz
Titre du livre: preferences, ein yankee aus conneticut, préférence, preference

Données de l'éditeur

Auteur: Johannes Fürnkranz; Eyke Hüllermeier
Titre: Preference Learning
Editeur: Springer; Springer Berlin
466 Pages
Date de parution: 2010-10-10
Berlin; Heidelberg; DE
Imprimé / Fabriqué en
Langue: Anglais
171,19 € (DE)
175,99 € (AT)
189,00 CHF (CH)
POD
IX, 466 p.

BB; Hardcover, Softcover / 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; Artificial Intelligence; Data Mining and Knowledge Discovery; 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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