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Notation and Code Examples.
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 Curve 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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dusty mcintye
reviewed A Statistical Approach to Neural Networks for Pattern Recognition on July 18, 2008
A Statistical Approach to Neural Networks for Pattern Recognition
Fascinating read but quite outdated
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