Neural Networks for Pattern Recognition

Auteur(s) : Christopher M. Bishop
Editeur : Oxford University Press

Nombre de pages : 482 pages
Date de parution : 10/11/1995

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Résumé

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. 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.

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
7. Parameter optimization algorithms
8. Pre-processing and feature extraction
9. Learning and generalization
10. Bayesian techniques.