Articles liés à Foundations of Rule Learning
Synopsis
Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.
The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
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À propos de l?auteur
Prof. Dr. Johannes Fürnkranz is a professor of knowledge engineering at the Technische Universität Darmstadt. He has chaired and served on the boards of the main journals and conferences in this field. His research interests include inductive rule learning, preference learning, game playing, web mining, and data mining in social science.
Dr. Dragan Gamberger heads the Laboratory for Information Systems at the Rudjer Boskovic Institute in Zagreb. He has chaired the main related conference ECML/PKDD, and is a coauthor of the publicly available Data Mining Server. His research interests include data mining and the medical applications of descriptive rule induction.
Prof. Dr. Nada Lavrač heads the Department of Knowledge Technologies at the Jozef Stefan Institute in Ljubljana. She is the author and editor of several books and proceedings in the field of data mining and machine learning, and she has chaired or served on the boards of the main related journals and conferences. Her research interests include machine learning, data mining, and inductive logic programming, and related applications in medicine, public health, bioinformatics, and the management of virtual enterprises. In 1997 she was awarded the Ambassador of Science of Slovenia prize, and in 2007 she was elected as an ECCAI Fellow.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Foundations of Rule Learning
Edité par
Springer Berlin Heidelberg, 2014
ISBN 10 : 3642430465
ISBN 13 : 9783642430466
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fills a significant gap in the machine learning literatureExplains the most comprehensive knowledge representation formalismOffers researchers and graduate students a clear unifying terminologyProf. Dr. Johannes Fuernkranz . N° de réf. du vendeur 5060558
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Foundations of Rule Learning
Edité par
Springer Berlin Heidelberg, 2014
ISBN 10 : 3642430465
ISBN 13 : 9783642430466
Neuf
Taschenbuch
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Rules - the clearest, most explored and best understood form of knowledge representation - are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data. N° de réf. du vendeur 9783642430466
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Foundations of Rule Learning
Edité par
Springer Berlin Heidelberg Dez 2014, 2014
ISBN 10 : 3642430465
ISBN 13 : 9783642430466
Neuf
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Rules - the clearest, most explored and best understood form of knowledge representation - are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data. 352 pp. Englisch. N° de réf. du vendeur 9783642430466
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Foundations of Rule Learning
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Dez 2014, 2014
ISBN 10 : 3642430465
ISBN 13 : 9783642430466
Neuf
Taschenbuch
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Taschenbuch. Etat : Neu. Neuware -Rules ¿ the clearest, most explored and best understood form of knowledge representation ¿ are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning.The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch. N° de réf. du vendeur 9783642430466
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Foundations of Rule Learning (Cognitive Technologies)
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Foundations of Rule Learning (Cognitive Technologies)
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Foundations of Rule Learning (Cognitive Technologies)
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Foundations of Rule Learning (Cognitive Technologies)
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Paperback. Etat : Brand New. 2012 edition. 352 pages. 9.30x6.20x0.80 inches. In Stock. N° de réf. du vendeur __3642430465
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Foundations of Rule Learning (Cognitive Technologies)
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Paperback. Etat : Brand New. 2012 edition. 352 pages. 9.30x6.20x0.80 inches. In Stock. N° de réf. du vendeur 3642430465
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Foundations of Rule Learning (Cognitive Technologies)
Edité par
Springer, 2014
ISBN 10 : 3642430465
ISBN 13 : 9783642430466
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