BISHOP:NEURAL NETWORKS FOR PATTERN RECOGNITION PAPER (Advanced Texts in Econometrics (Paperback))
Author(s): C.M. Bishop (Author)
Publisher: Oxford University Press
Publication Date: 23 Nov. 1995
Edition: Illustrated
Language: English
Print length: 502 pages
ISBN-10: 9780198538646
ISBN-13: 9780198538646
Book Description
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.
Editorial Reviews
Amazon Review
This book provides a solid statistical foundation for neural networks from a pattern-recognition perspective. The focus is on the types of neural nets that are most widely used in practical applications, such as the multi-layer perceptron and radial basis function networks. Rather than trying to cover many different types of neural networks, Christopher Bishop thoroughly covers topics such as density estimation, error functions, parameter optimisation algorithms, data pre-processing and Bayesian methods. All topics are organised well and all mathematical foundations are explained before being applied to neural networks. The text is suitable for a graduate or advanced undergraduate level course on neural networks or for practitioners interested in applying neural networks to real-world problems. The reader is assumed to have the level of mathematical knowledge necessary for an undergraduate science degree. —Jake Bond
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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
Book Description
The first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.
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