Neural Networks for Pattern Recognition / Edition 1

Paperback (Print)


This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Editorial Reviews

From the Publisher
"Should be in the library of any student, teacher, or researcher with a keen interest in modern statistical methods, a large volume of meaningful data to analyze (including simulations), and a fast workstation with good numerical and graphical capabilities."—Journal of the American Statistical Association

"....should be warmly welcomed by the neural network and pattern recognition communities. Bishop can be recommended to students and engineers in computer science."—Computer Journal

"An excellent and rigorous treatment of a number of neural network architectures."—Journal of Mathematical Psychology

"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 first-class book for the researcher in statistical pattern recognition."—Times Higher Education Supplement

"Although there has been a plethora of books on neural networks published in the last five years, none has really addressed the subject with the necessary mathematical rigour. Professor Bishop's book is the first textbook to provide a clear and comprehensive treatment of the mathematical principles underlying the main types of artificial neural networks."—Dr. L. Tarassenko and Professor J.M. Brady, Department of Engineering Science, University of Oxford

"There has been an acute need for authoritative textbooks in neural networks that explain the main ideas clearly and consistently using the basic tools of linear algebra, calculus, and simple probability theory. There have been many attempts to provide such a text, but until now, none has succeeded. This is a serious attempt at providing such an ideal textbook. By concentrating on pattern recognition aspects of neural works, the author is able to treat many important topics in much greater depth. The most important contribution of the book is the solid statistical pattern recognition approach, a sign of increasing maturity in the field."—Mathematical Reviews

"The following keywords concisely indicate the contents: artificial neural networks, statistical pattern recognition, probability density estimation, single-layer networks, multi-layer perception, radial basis functions, error functions, parameter optimization algorithms, Bayesian techniques, etc. The book is aimed at researchers and practitioners. It can also be used as the primary text in a course for graduate students (129 graded exercises!)."—Industrial Mathematics

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Product Details

  • ISBN-13: 9780198538646
  • Publisher: Oxford University Press, USA
  • Publication date: 1/18/1996
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 504
  • Sales rank: 1,293,859
  • Product dimensions: 9.19 (w) x 6.19 (h) x 1.12 (d)

Table of Contents

1 Statistical Pattern Recognition 1
2 Probability Density Estimation 33
3 Single-Layer Networks 77
4 The Multi-layer Perceptron 116
5 Radial Basis Functions 164
6 Error Functions 194
7 Parameter Optimization Algorithms 253
8 Pre-processing and Feature Extraction 295
9 Learning and Generalization 332
10 Bayesian Techniques 385
Symmetric Matrices 440
Gaussian Integrals 444
Lagrange Multipliers 448
Calculus of Variations 451
Principal Components 454
References 457
Index 477
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Customer Reviews

Average Rating 5
( 5 )
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Sort by: Showing all of 6 Customer Reviews
  • Anonymous

    Posted November 9, 2003

    Great Book

    I didn't know anything about neural networks when I bought this book. It was a very hard and slow read, but on my third attempt things started to fall into place. Now I'm a whiz and this book did it all. A great review of many different angles and you can traslate it all into useful applications

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  • Anonymous

    Posted August 20, 2003

    Great book to go with.

    Great book to read through to get full understanding of neural networks and about what what they do and how they do it. It pretty mathematical which is a GOOD thing most books about the topic give a very schematic approach to it. If you know your calculus you should be fine and even if you don't it goes over how to solve most of it. My only complaint ( not with this book but in all books that does this) is the lack of answers for the exercises. I've always liked to check my work. But thats a small problem. This book is GREAT!!! go out and get it now.

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  • Anonymous

    Posted February 1, 2002

    master thesis

    I'd like to apply the pattern recognition on the electrical power system especialy on the switchgear devices. so I'd like to increase my background about it.

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  • Anonymous

    Posted February 16, 2001

    excellent book

    a compact book covering all the essential items. very well written. author has done a good job. this book is for technical research people.

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  • Anonymous

    Posted September 11, 2000

    A Great Book

    This book helps a lot in understanding how a basic structure of neural network can be used practically in the industry. Mr.Bishop has done a good job in making it as simple as possible for people with very little background to understand it very well.

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  • Anonymous

    Posted December 25, 1999

    Clear, insightful

    A clear, insightful description of the basic theory behind neural networks and its application to pattern recognition and function regression. It also does a good job describing problems one might encounter in using neural networks by tying together many ideas from the field of neural network with those from statistical/probability theory and regression theory.

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