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.
""The book provides an excellent introduction to neutral networks from a statistical perspective."" (International Statistical Review, 2008)
""Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering."" (Mathematical Reviews)