Description: Neural Networks for Pattern RecognitionAuthor(s): Christopher M. Bishop, Geoffrey Hinton Format: Paperback Publisher: Oxford University Press, United Kingdom Imprint: Clarendon Press ISBN-13: 9780198538646, 978-0198538646 Synopsis This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts of pattern recognition, the book describes techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. It also motivates the use of various forms of error functions, and reviews the principal algorithms for error function minimization. As well as providing a detailed discussion of learning and generalization in neural networks, the book also covers the important topics of data processing, feature extraction, and prior knowledge. The book concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.
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Book Title: Neural Networks for Pattern Recognition
Number of Pages: 504 Pages
Language: English
Publication Name: Neural Networks for Pattern Recognition
Publisher: Oxford University Press
Publication Year: 1995
Subject: Computer Science, Mathematics
Item Height: 234 mm
Item Weight: 751 g
Type: Textbook
Author: Christopher M. Bishop
Item Width: 156 mm
Format: Paperback
