9783540420279 - logic for learning: learning comprehensible theories from structured data (cognitive technologies) von lloyd, john w. (5 Ergebnisse)
Sprache: Englisch
Verlag: Springer 2003
Serie: Cognitive Technologies, Buch 32 von 43. Buch 32 von 43 - Cognitive Technologies
- Hardcover
Anbieter: Anybook.com, Lincoln, Vereinigtes KönigreichAnybook.com
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EUR 25,67
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Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:9783540420279.
Sprache: Englisch
Verlag: Springer 2003
Serie: Cognitive Technologies, Buch 32 von 43. Buch 32 von 43 - Cognitive Technologies
- Hardcover
Anbieter: Roland Antiquariat UG haftungsbeschränkt, Weinheim, DeutschlandRoland Antiquariat UG haftungsbeschränkt
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2003. 267 p. Unread book. Like new. 9783540420279 Sprache: Englisch Gewicht in Gramm: 522 Hardcover: 23.4 x 1.6 x 15.6 cm.
Sprache: Englisch
Verlag: Springer 2003
Serie: Cognitive Technologies, Buch 32 von 43. Buch 32 von 43 - Cognitive Technologies
- Hardcover
Anbieter: Ria Christie Collections, Uxbridge, Vereinigtes KönigreichRia Christie Collections
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Zustand: New. In.
Sprache: Englisch
Verlag: Springer, Springer Vieweg 2003
Serie: Cognitive Technologies, Buch 32 von 43. Buch 32 von 43 - Cognitive Technologies
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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Buch. Zustand: 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 computatio…nal 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.
Sprache: Englisch
Verlag: Berlin Springer Verlag 2003
Serie: Cognitive Technologies, Buch 32 von 43. Buch 32 von 43 - Cognitive Technologies
- Hardcover
Anbieter: CSG Onlinebuch GMBH, Darmstadt, , DeutschlandCSG Onlinebuch GMBH
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Gebunden. Zustand: 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 tho…se 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.
