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.
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SUMMARY OR ABSTRACT
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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.
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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.
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Wiley InterScience
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10.1002/9780470148150
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Title
Statistical approach to neural networks for pattern recognition.