Da: Roland Antiquariat UG haftungsbeschränkt, Weinheim, Germania
EUR 29,90
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Aggiungi al carrello2003. 267 p. Unread book. Like new. 9783540420279 Sprache: Englisch Gewicht in Gramm: 522 Hardcover: 23.4 x 1.6 x 15.6 cm.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 53,38
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 53,40
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 52,24
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 52,24
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Da: Best Price, Torrance, CA, U.S.A.
EUR 48,25
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Da: Best Price, Torrance, CA, U.S.A.
EUR 48,25
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Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 60,59
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 60,81
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Da: Literary Cat Books, Machynlleth, Powys, WALES, Regno Unito
Membro dell'associazione: IOBA
EUR 52,03
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Aggiungi al carrelloHardcover. Condizione: Near Fine. Condizione sovraccoperta: No Dust Jacket. (?); (?). This textbook presents a systematic approach to knowledge representation, computation, and learning using higher-order logic. It is aimed at researchers, graduate students, and senior undergraduates working in computational logic and/or machine learning. The book does not assume previous knowledge of either field. It is part of the Springer series Cognitive Technologies, which addresses subjects including natural-language processing, high-level computer vision, cognitive robotics, automated reasoning, and knowledge representation. ; 16x23.5x2cm; 267 pages.
Da: CSG Onlinebuch GMBH, Darmstadt, Germania
EUR 32,64
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Aggiungi al carrelloGebunden. Condizione: Gut. Gebraucht - Gut Zustand: Gut, Mängelexemplar, X, 256 p. 14 illus. About this book: This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed, and for those in machine learning no previous knowledge of computational logic is assumed.The logic used throughout the book is a higher-order one, since higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, especially those who study learning methods for structured data. Throughout, great emphasis is placed on learning comprehensible theories. The book serves as an introduction for computational logicians to machine learning, a particularly interesting and important application area of logic, and also provides a foundation for functional logic programming languages. Written for advanced students, researchers, scientists.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 58,09
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 58,09
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,08
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 58,08
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 65,72
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Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 65,80
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlanda
EUR 73,46
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Aggiungi al carrelloCondizione: New. 2003. 2003rd Edition. hardcover. . . . . .
Da: Kennys Bookstore, Olney, MD, U.S.A.
EUR 90,81
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Aggiungi al carrelloCondizione: New. 2003. 2003rd Edition. hardcover. . . . . . Books ship from the US and Ireland.
Editore: Springer Berlin Heidelberg, 2010
ISBN 10: 3642075533 ISBN 13: 9783642075537
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning.
Editore: Springer Berlin Heidelberg, 2003
ISBN 10: 3540420274 ISBN 13: 9783540420279
Lingua: Inglese
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
Convertire valutaQuantità: 1 disponibili
Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. The intended audience is senior undergraduates, graduate students, and researchers in either of those fields. For those in computational logic, no previous knowledge of machine learning is assumed and, for those in machine learning, no previous knowledge of computational logic is assumed. The logic used throughout the book is a higher-order one. Higher-order logic is already heavily used in some parts of computer science, for example, theoretical computer science, functional programming, and hardware verifica tion, mainly because of its great expressive power. Similar motivations apply here as well: higher-order functions can have other functions as arguments and this capability can be exploited to provide abstractions for knowledge representation, methods for constructing predicates, and a foundation for logic-based computation. The book should be of interest to researchers in machine learning, espe cially those who study learning methods for structured data. Machine learn ing applications are becoming increasingly concerned with applications for which the individuals that are the subject of learning have complex struc ture. Typical applications include text learning for the World Wide Web and bioinformatics. Traditional methods for such applications usually involve the extraction of features to reduce the problem to one of attribute-value learning.