Neural networks for pattern recognition

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-layerperceptron 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 100exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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

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