Neural Networks for Pattern Recognition

Description

Neural Networks for Pattern Recognition

Publisher: Oxford University Press, USA
Number Of Pages: 500
Publication Date: 1995-11-03
Sales Rank: 110077
ISBN / ASIN: 0198538642
EAN: 9780198538646
Binding: Paperback
Manufacturer: Oxford University Press, USA
Studio: Oxford University Press, USA
Average Rating: 5

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, Bishop thoroughly covers topics such as density estimation, error functions, parameter optimization algorithms, data pre-processing, and Bayesian methods. All topics are organized 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 math knowledge necessary for an undergraduate science degree.

This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. The author introduces the basic principles of pattern recognition and then goes on to describe techniques for modelling probability density functions, and discusses the properties and relative merits of the multi-layer perceptron and radial basis function network models. This book is designed with graduate students in mind and throughout the text it motivates the use of various forms of error functions and reviews the principal algorithms for error function minimization. Bishop also covers the fundamental topics of data processing, feature extraction, and prior knowledge and concludes with an extensive treatment of Bayesian techniques and their applications to neural networks.

Review:

Only for an expert

Mr Bishop's book is very well written and contains a lot of useful information on neural networks. It is outlined well and progresses in a logical form. If, however, you are looking for a book that gives discussions with concrete examples of neural networks applications or set ups, you will be sorely disappointed. The mathematical treatment is universally generalized with very few specific concrete examples shown. Even the exercises will not serve you well. The term 'graded' is used; however, that simply referes to the description of difficulty. There are no answers to these exercises, so unless you have a teacher or are already firmly familiar with the material, you will not know if you have completed them correctly or not. Even worse, the exercises are in general not written to reinforce concepts in the chapter, but in most cases extend the chapter material into new regions.

In summary, this book should only be purchased by someone already familiar with neural networks and their mathematical basis. Anyone else will be wasting their money.

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