Logic for Learning - Learning Comprehensible Theories from Structured Data (Paperback, Softcover reprint of hardcover 1st ed. 2003)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Cognitive Technologies
Release date:	October 2010
First published:	2003
Authors:	John W. Lloyd
Dimensions:	235 x 155 x 18mm (L x W x T)
Format:	Paperback
Pages:	257
Edition:	Softcover reprint of hardcover 1st ed. 2003
ISBN-13:	978-3-642-07553-7
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-642-07553-3
Barcode:	9783642075537

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