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Neural Networks for Pattern Recognition

By: C.M. Bishop
Binding: Paperback
Publisher: Clarendon Press
ISBN: 0198538642
ISBN-13: 9780198538646
Released: 23 Nov 1995
RRP: £39.50
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Customer Reviews

A good introduction, lacks detail and generality. - By: Christos Dimitrakakis, 18 Jun 2004
This is good & quite clear introduction to the field that tries to give the reader an intuitive overview to the neural networks & pattern recognition in general.

This is a good book if you are interested in a conversationalist overview to neural networks. There are sufficient formulas to implement the algorithms, so it is good as a list of commonly used neural architectures & how they work, in a single easy-to-access place.

However, the book is quite short & hurriedly goes through many different techniques & algorithms, giving you a brief snapshot of each one. Nice pictures abound & explanations, but the understanding that one may obtained from this book will be only superficial. Since the book does not discuss the foundations behind each technique, most of them appear disjoint & unrelated.

Actuallly, the lack of detail & mathematical rigour can be confusing. The need to explain concepts intuitively is hardly an excuse, since there exist other books that manage to achieve clarity, easy of understanding & mathematical rigour, while they develop concepts with sufficient generality for the student to fully grasp the relation between various methods.

From my own viewpoint, supervised neural network learning is just a special case of optimisation (the quantity to be optimised is the neural network parameter) under statistical uncertainty (the cost function to be minimised is only partiallly defined by a set of data & needs to be estimated).
Thus, in addition to this book I also recommend taking a look at Bertseka's "Constrained optimization & Lagrange multiplier methods" & his newer "Nonlinear Pogramming" book. His "Neuro-Dynamic programming" book covers a lot more than just neural networks for pattern recognition. Advanced readers that are also interested in optimal stochastic control & reinforcement learning will find it useful.

All in alll, recommended for people that simply want to implement some neural network algorithms or for people that want a quick introduction. It is advisable, however, to keep a couple of books on estimation theory & on optimisation theory as an aid to deeper understanding.

Without doubt the best book available on Neural Computing. - By: , 30 Aug 1999
Bishop's book is the current bible on Neural Computing. It is superbly written & presented, & the subject material carefully selected. The ideas of neural computing are motivated from a statistical pattern recognition point of view, though the reader is not expected to have a strong foundation in probability theory - just a basic appreciation is enough to begin with. The book has enormous (though not excessive) breadth, & covers practicallly every aspect of tradiational neural networks, from theoretical aspects motivated by probability theory, to practical concerns about optimisation & learning, & finallly to a more advanced treatement on Bayesian methods. Above alll, Bishop's writing is lucid & clear, & although some of the topics are conceptuallly intricate, they are always readable & accessible. Buy this book if you have anything to do with neural networks!