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Neural Networks for Pattern Recognition (Paperback)

by C.M. Bishop (Author) "The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters,..." (more)

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Product details

Paperback: 504 pages
Publisher: Clarendon Press (23 Nov 1995)
Language English
ISBN-10: 0198538642
ISBN-13: 978-0198538646
Product Dimensions: 22.9 x 15.5 x 2.8 cm

Average Customer Review: 4.0 out of 5 stars See all reviews (2 customer reviews)

Amazon.co.uk Sales Rank: 328,507 in Books (See Bestsellers in Books)

Popular in these categories:

#29 in	Books > Computing & Internet > Computer Science > Algorithms > Fuzzy Logic
#31 in	Books > Science & Nature > Popular Science > Artificial Intelligence
#50 in	Books > Computing & Internet > Computer Science > Artificial Intelligence > Neural Networks

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Product Description

Amazon.co.uk 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

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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First Sentence
The term pattern recognition encompasses a wide range of information processing problems of great practical significance, from speech recognition and the classification of handwritten characters, to fault detection in machinery and medical diagnosis. Read the first page

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23 of 24 people found the following review helpful:

5.0 out of 5 stars Without doubt the best book available on Neural Computing., 30 Aug 1999

By A Customer

Bishop's book is the current bible on Neural Computing. It is superbly written and presented, and 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, and covers practically every aspect of tradiational neural networks, from theoretical aspects motivated by probability theory, to practical concerns about optimisation and learning, and finally to a more advanced treatement on Bayesian methods. Above all, Bishop's writing is lucid and clear, and although some of the topics are conceptually intricate, they are always readable and accessible. Buy this book if you have anything to do with neural networks!

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12 of 13 people found the following review helpful:

3.0 out of 5 stars A good introduction, lacks detail and generality., 18 Jun 2004

By	Christos Dimitrakakis "olethrosdc" - See all my reviews (REAL NAME)

This is good and quite clear introduction to the field that tries to give the reader an intuitive overview to the neural networks and 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 and how they work, in a single easy-to-access place.

However, the book is quite short and hurriedly goes through many different techniques and algorithms, giving you a brief snapshot of each one. Nice pictures abound and 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 and unrelated.

Actually, the lack of detail and 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 and 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 partially defined by a set of data and needs to be estimated).
Thus, in addition to this book I also recommend taking a look at Bertseka's "Constrained optimization and Lagrange multiplier methods" and 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 and reinforcement learning will find it useful.

All in all, 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 and on optimisation theory as an aid to deeper understanding.

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