Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition

Paperback Advanced Texts in Econometrics (Paperback)

By (author) C.M. Bishop, Volume editor Geoffrey E. Hinton, By (author) Geoffrey Hinton

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  • Publisher: Clarendon Press
  • Format: Paperback | 504 pages
  • Dimensions: 155mm x 229mm x 28mm | 748g
  • Publication date: 18 January 1996
  • Publication City/Country: Oxford
  • ISBN 10: 0198538642
  • ISBN 13: 9780198538646
  • Illustrations note: line figures
  • Sales rank: 332,847

Product description

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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Review quote

excellent... Bishop is able to achieve a level of depth on these topics which is unparalleled in other neural-net texts... clear and concise mathematical analysis. Bishop's text [] picks up where Duda and Hart left off, and, luckily does so with the same level of clarity and elegance. Neural Networks for Pattern Recognition is an excellent read, and represents a real contribution to the neural-net community. IEEE Transactions on Neural Networks, May 1997 this is an excellent book in the specialised area of statistical pattern recognition with statistical neural nets ... a good starting point for new students in those laboratories where research into statistico-neural pattern recognition is being done ... The examples for the reader at the end of this and every chapter are well chosen and will ensure sales as a course textbook ... this is a first-class book for the researcher in statistical pattern recognition. Times Higher Bishop leads the way through a forest of mathematical minutiae. Readers will emerge with a rigorous statistical grounding in the theory of how to construct and train neural networks in pattern recognition. New Scientist [Bishop] has written a textbook, introducing techniques, relating them to the theory, and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book... should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science. The Computer Journal, Volume 39, No. 6, 1996 Its sequential organization and end-of chapter exercises make it an ideal mental gymnasium. The author has eschewed biological metaphor and sweeping statements in favour of welcome mathematical rigour. Scientific Computing World a neural network introduction placed in a pattern recognition context. ...He has written a textbook, introducing techniques, relating them to the theory and explaining their pitfalls. Moreover, a large set of exercises makes it attractive for the teacher to use the book ... should be warmly welcomed by the neural network and pattern recognition communities. Robert P. W. Duin, IAPR Newsletter Vol. 19 No. 2 April 1997 This outstanding book contributes remarkably to a better statistical understanding of artificial neural networks. The superior quality of this book is that it presents a comprehensive self-contained survey of feed-forward networks from the point of view of statistical pattern recognition. Zbl.Math 868

Table of contents

1. Statistical pattern recognition ; 2. Probability density estimation ; 3. Single-layer networks ; 4. The multi-layer perceptron ; 5. Radial basis functions ; 6. Error functions ; 7. Parameter optimization algorithms ; 8. Pre-processing and feature extraction ; 9. Learning and generalization ; 10. Bayesian techniques