A Statistical Approach to Neural Networks for Pattern Recognition

Preface.

Acknowledgments.

1. Introduction.

2. The Multi-Layer Perception Model.

3. Linear Discriminant Analysis.

4. Activation and Penalty Functions.

5. Model Fitting and Evaluation.

6. The Task-Based MLP.

7. Incorporating Spatial Information into an MLP Classifier.

8. Influence Curves for the Multi-Layer Perceptron Classifier.

9. The Sensitivity Curves of the MLP Classifier.

10. A Robust Fitting Procedure for MLP Models.

11. Smoothed Weights.

12. Translation Invariance.

13. Fixed-slope Training.

Appendix A. Function Minimization.

Appendix B. Maximum Values of the Influence Curve.

Topic Index.