Description
Book SynopsisThis book presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models, in a language that is familiar to practicing statisticians. Questions arise when statisticians are first confronted with such a model, and this book's aim is to provide thorough answers.
Trade Review"This book is a good introduction to neural networks for a statistician." (
Journal of the American Statistical Association, March 2009)
"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)
Table of ContentsNotation 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.
