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Synopsis
This book provides an 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.
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Logic for Learning : Learning Comprehensible Theories from Structured Data
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Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
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Logic for Learning (Paperback)
Edité par
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10 : 3642075533
ISBN 13 : 9783642075537
Neuf
Paperback
Edition originale
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Paperback. Etat : new. Paperback. 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. This book is concerned with the rich and fruitful interplay between the fields of computational logic and machine learning. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783642075537
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Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
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Logic for Learning : Learning Comprehensible Theories from Structured Data
Edité par
Springer, 2010
ISBN 10 : 3642075533
ISBN 13 : 9783642075537
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Logic for Learning : Learning Comprehensible Theories from Structured Data
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Logic for Learning
Edité par
Springer Berlin Heidelberg Okt 2010, 2010
ISBN 10 : 3642075533
ISBN 13 : 9783642075537
Neuf
Taschenbuch
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 -This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it provides a framework for knowledge representation and computation based on higher-order logic, and demonstrates its advantages over more standard approaches based on first-order logic. For those interested in machine learning, the book explains how higher-order logic provides suitable knowledge representation formalisms and hypothesis languages for machine learning applications. 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. 272 pp. Englisch. N° de réf. du vendeur 9783642075537
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Logic for Learning
Edité par
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, 2010
ISBN 10 : 3642075533
ISBN 13 : 9783642075537
Neuf
Couverture souple
Vendeur : Kennys Bookshop and Art Galleries Ltd., Galway, GY, Irlande
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Etat : New. Series: Cognitive Technologies. Num Pages: 267 pages, biography. BIC Classification: UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 14. Weight in Grams: 421. . 2010. Softcover reprint of hardcover 1st ed. 2003. Paperback. . . . . N° de réf. du vendeur V9783642075537
Quantité disponible : 15 disponible(s)
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Logic for Learning : Learning Comprehensible Theories from Structured Data
Edité par
Springer, 2010
ISBN 10 : 3642075533
ISBN 13 : 9783642075537
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
Évaluation du vendeur 5 sur 5 étoiles
Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 11874534
Quantité disponible : Plus de 20 disponibles