# eBook Neural Networks for Pattern Recognition download

## by Christopher M. Bishop

**ISBN:**0198538499

**Author:**Christopher M. Bishop

**Publisher:**Oxford University Press (January 18, 1996)

**Language:**English

**Pages:**504

**ePub:**1837 kb

**Fb2:**1398 kb

**Rating:**4.9

**Other formats:**mobi mbr txt doc

**Category:**Technologies

**Subcategory:**Computer Science

Christopher M. Bishop. Dr. Bishop is a world-renowned expert in this field, but his book didn't work for me. Despite the title, it covers the more general topic of classification, not just Neural Networks.

Christopher M.

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. 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. L. Tarassenko and Professor . Brady, Department of Engineering Science, University of Oxford.

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 modelling 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.

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. This book provides the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition.

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From the perspective of pattern recognition, neural networks can be regarded as an extension of the many conventional techniques which have been developed over several decades. Indeed, this book includes discussions of several concepts in conventional statistical pattern recognition which I regard as essential for a clear understanding of neural networks. More extensive treatments of these topics can be found in the many texts on statistical pattern recognition, including Duda and Hart (1973), Hand (1981), Devijver and Kifctler (1982), and Fiikunaga (1990).

Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them. 1. Data Mining: Know It All. Bishop, Geoffrey Hinton.

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