Foundations of Rule Learning: Essentials of Machine Learning and Relational Data Mining - Couverture rigide

F�rnkranz, Johannes; Gamberger, Dragan; Lavrač, Nada

9783540751960: Foundations of Rule Learning: Essentials of Machine Learning and Relational Data Mining

Couverture rigide

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

Editeur : Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2012

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This book reviews the basics of rule learning as applied to classical machine learning and modern data mining. It connects attribute-value learning with inductive logic programming, and offers complete coverage of most important elements of rule learning.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

� propos de l?auteur

Prof. Dr. Johannes F�rnkranz is a professor of knowledge engineering at the Technische Universit�t 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 r�f�rence � une autre �dition de ce titre.

�diteur: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Date d'�dition: 2012
Langue: anglais
ISBN 10: 3540751963
ISBN 13: 9783540751960
Reliure: Reli�
Nombre de pages: 334
Coordonn�es du fabricant: non disponible
Personne responsable: CMN Printpool Inh. Mathis Neumann
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Foundations of Rule Learning

Johannes F�rnkranz|Dragan Gamberger|Nada Lavrac

Edit� par Springer Berlin Heidelberg, 2012

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

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Gebunden. 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 r�f. du vendeur 4900147

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Foundations of Rule Learning (Cognitive Technologies)

F�rnkranz, Johannes; Gamberger, Dragan; Lavra?, Nada

Edit� par Springer, 2012

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

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Foundations of Rule Learning

Johannes F�rnkranz

Edit� par Springer Berlin Heidelberg, 2012

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

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Buch. 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 r�f. du vendeur 9783540751960

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Foundations of Rule Learning

Johannes F�rnkranz

Edit� par Springer Berlin Heidelberg Nov 2012, 2012

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

Neuf Couverture rigide

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Buch. 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 r�f. du vendeur 9783540751960

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Foundations of Rule Learning

Johannes F�rnkranz

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2012, 2012

ISBN 10 : 3540751963 ISBN 13 : 9783540751960

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. 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 r�f. du vendeur 9783540751960

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