Notation and Code Examples. 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.
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