Neural Networks for Pattern Recognition (Paperback)

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. The author introduces the basic principles of pattern recognition and then goes on to describe techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. This book is designed with graduate students in mind and throughout the text it motivates the use of various forms of error functions and reviews the principal algorithms for error function minimization. Bishop also covers the fundamental topics of data processing, feature extraction, and prior knowledge and concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

General

Imprint:	Clarendon Press
Country of origin:	United Kingdom
Release date:	November 1995
First published:	1996
Authors:	Christopher M. Bishop
Foreword by:	Geoffrey Hinton
Dimensions:	234 x 157 x 25mm (L x W x T)
Format:	Paperback
Pages:	482
ISBN-13:	978-0-19-853864-6
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Applications of computing > Pattern recognition Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
LSN:	0-19-853864-2
Barcode:	9780198538646

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