Neural Networks for Pattern Recognition / Edition 1

Neural Networks for Pattern Recognition / Edition 1

4.8 5
by Christopher M. Bishop, C. M. Bishop
     
 

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 modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various

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Overview

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 modeling 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. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

Product Details

ISBN-13:
9780198538646
Publisher:
Oxford University Press, USA
Publication date:
01/18/1996
Edition description:
New Edition
Pages:
504
Product dimensions:
9.19(w) x 6.19(h) x 1.12(d)

Table of 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
A. Symmetric Matrices
B. Gaussian Integrals
C. Lagrange Multipliers
D. Calculus of Variations
E. Principal Components
References
Index

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