A Statistical Approach to Neural Networks for Pattern Recognition

by Robert A. Dunne

An accessible and up-to-date treatment featuring the connection between neural networks and statistics A Statistical Approach to Neural Networks for Pattern Recognition presents a statistical treatment of the Multilayer Perceptron (MLP), which is the most widely used of the neural network models. This book aims to answer questions that arise when statisticians are first confronted with this type of model, such 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? Thorough answers to these questions and many more are included, as well as worked examples and selected problems for the reader. Discussions on the use of MLP models with spatial and spectral data are also included. Further treatment of highly important principal aspects of the MLP are provided, such as the robustness of the model in the event of outlying or atypical data; the influence and sensitivity curves of the MLP; why the MLP is a fairly robust model; and modifications to make the MLP more robust. The author also provides clarification of several misconceptions that are prevalent in existing neural network literature. Throughout the book, the MLP model is extended in several directions to show that a statistical modeling approach can make valuable contributions, and further exploration for fitting MLP models is made possible via the R and S-PLUS® codes that are available on the book's related Web site. A Statistical Approach to Neural Networks for Pattern Recognition successfully connects logistic regression and linear discriminant analysis, thus making it a critical reference and self-study guide for students and professionals alike in the fields of mathematics, statistics, computer science, and electrical engineering.

288 pages, Hardcover

First published January 1, 2007

Reviews

[Demitkecil]

Published by John Wiley & Son, 2007



The author of this book Robert A. Dunne, PhD, is Research Scientist in the Mathematical and Information Sciences Division of the Commonwealth Scientific and Industrial Research Organization (CSIRO) in North Ryde, Australia. Dr. Dunne received his PhD from Murdoch University, and his research interests include remote sensing and bioinformatics



This book provides an excellent introduction to neutral networks from a statistical perspective. Even tough statistic is not easy to understand easily, this book 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.



This book organized into 13 chapters :



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



As statistic and neural network book, this book is easy enogh to understand.