A Statistical Approach to Neural Networks for Pattern Recognition; Contents; Notation and Code Examples; Preface; Acknowledgments; 1 Introduction; 2 The Multi-Layer Perceptron 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.
0
This 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. The following are a few questions that are considered in this book and are explored: how robust is the model to outliers, could the model be made more robust, which points will have a high leverage, what are good starting values for the fitting algorithm, etc. Discussions i.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Wiley InterScience
10.1002/9780470148150
Statistical approach to neural networks for pattern recognition.