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Sinopsis
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.
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Críticas
From the reviews of the third edition:
"John has tried his hand at machine learning, and his aim in Logic for Learning is to demonstrate ‘the rich and fruitful interplay between the fields of computational logic and machine learning’. ... As such, the book is more geared towards computational logicians who are interested in machine learning ... . The book can also be used as a textbook in a mathematically oriented advanced graduate course. ... it is indeed great stuff, which deserves to be taken serious by any computational logician ... ." (Peter Flach, TLP – Theory and Practice of Logic Programming, Issue 4, 2004)
From the reviews:
"This book provides 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." (PHINEWS, Vol. 3, April, 2003)
Reseña del editor
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.
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Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
Librería: Roland Antiquariat UG haftungsbeschränkt, Weinheim, Alemania
Calificación del vendedor: 5 de 5 estrellas
2003. 267 p. Unread book. Like new. 9783540420279 Sprache: Englisch Gewicht in Gramm: 522 Hardcover: 23.4 x 1.6 x 15.6 cm. Nº de ref. del artículo: 201575
Cantidad disponible: 1 disponibles
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Logic for Learning: Learning Comprehensible Theories from Structured Data.
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Berlin Springer Verlag, 2003
ISBN 10: 3540420274
ISBN 13: 9783540420279
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Librería: CSG Onlinebuch GMBH, Darmstadt, Alemania
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Gebunden. Condición: 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. Nº de ref. del artículo: 18644
Cantidad disponible: 1 disponibles
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Logic For Learning
Librería: Swan Trading Company, GEORGETOWN, TX, Estados Unidos de America
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hardcover. Condición: Good. Hardcover ex-library with typical marks shows moderate cover wear. Text is unmarked. Ships FAST! Nº de ref. del artículo: 2411200017
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Logic For Learning
Librería: Amazing Books Pittsburgh, Pittsburgh, PA, Estados Unidos de America
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hardcover. Condición: Very Good. prev owners name on first page in pen. otherwise clean, sturdy, and unmarked - rw. Nº de ref. del artículo: Sq39502
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Logic for Learning: Learning Comprehensible Theories from Structured Data (Cognitive Technologies)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783540420279_new
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Logic for Learning
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Springer Berlin Heidelberg Aug 2003, 2003
ISBN 10: 3540420274
ISBN 13: 9783540420279
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. Nº de ref. del artículo: 9783540420279
Cantidad disponible: 2 disponibles
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Logic for Learning : Learning Comprehensible Theories from Structured Data
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Springer Berlin Heidelberg, 2003
ISBN 10: 3540420274
ISBN 13: 9783540420279
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: 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. Nº de ref. del artículo: 9783540420279
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Logic for Learning - Cognitive Technologies - Learning Comprehensible Theories from Structured Data
Publicado por
Springer, Germany, 2003
ISBN 10: 3540420274
ISBN 13: 9783540420279
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Librería: Literary Cat Books, Machynlleth, Powys, WALES, Reino Unido
Calificación del vendedor: 4 de 5 estrellas
Hardcover. Condición: Near Fine. Estado de la sobrecubierta: 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. Nº de ref. del artículo: 64612
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Logic for Learning
Publicado por
Springer Berlin Heidelberg, 2003
ISBN 10: 3540420274
ISBN 13: 9783540420279
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 4 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The first book on learning and knowledge representation based on higher-order logic.This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it. Nº de ref. del artículo: 4889573
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Logic and Learning : Learning Comprehensible Theories from Structured Data
Librería: GreatBookPrices, Columbia, MD, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Nº de ref. del artículo: 980623-n
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