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E-mail Message: I thought you might be interested in this item at http://www.worldcat.org/oclc/33101074 Title: Neural networks for pattern recognition Author: Christopher M Bishop Publisher: Oxford : New York : Clarendon Press ; Oxford University Press, 1995. ISBN/ISSN: 0198538499 9780198538493 0198538642 9780198538646 OCLC:33101074

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Neural networks for pattern recognition

Author:	Christopher M Bishop
Publisher:	Oxford : Clarendon Press ; New York : Oxford University Press, 1995.
Edition/Format:	Print book : EnglishView all editions and formats
Summary:	This is a 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.
Rating:	(not yet rated) 0 with reviews - Be the first.
Subjects	Neural networks (Computer science) Pattern recognition systems. Neural Networks (Computer) View all subjects
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Material Type:	Internet resource
Document Type:	Book, Internet Resource
All Authors / Contributors:	Christopher M Bishop Find more information about:
ISBN:	0198538499 9780198538493 0198538642 9780198538646
OCLC Number:	33101074
Description:	xvii, 482 pages : illustrations ; 24 cm
Contents:	1. Statistical Pattern Recognition -- 2. Probability Density Estimation -- 3. Single-Layer Networks -- 4. The Multi-layer Perceptron -- 5. Radial Basis Functions -- 6. Error Functions -- 7. Parameter Optimization Algorithms -- 8. Pre-processing and Feature Extraction -- 9. Learning and Generalization -- 10. Bayesian Techniques.
Responsibility:	Christopher M. Bishop.
More information:	Table of contents Publisher description The Classes of 1883 and 1884 Fund Home Page The John G. Hartman Memorial Library Fund Home Page

Abstract:

This book is the first to provide a comprehensive account of neural networks from a statistical perspective. Its emphasis is on pattern recognition, which currently represents the area of greatest Read more...

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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 Read more...

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