Machine Learning, Neural and Statistical Classification

The above book (originally published in 1994 by Ellis Horwood) is now out of print. The copyright now resides with the editors who have decided to make the material freely available on the web.
This book is based on the EC (ESPRIT) project StatLog which compare and evaluated a range of classification techniques, with an assessment of their merits, disadvantages and range of application. This integrated volume provides a concise introduction to each method, and reviews comparative trials in large-scale commercial and industrial problems. It makes accessible to a wide range of workers the complex issue of classification as approached through machine learning, statistics and neural networks, encouraging a cross-fertilization between these discplines.
The Whole Book (zipped PostScript - 0.73 Mb) The Whole Book (PDF format - 1.79 Mb)
Table of Contents

Chapter 1: Introduction

Chapter 2: Classification

Chapter 3: Classical Statistical Methods

Chapter 4: Modern Statistical Techniques

Chapter 5: Machine Learning of Rules and Trees

Chapter 6: Neural Networks

Chapter 7: Methods for Comparison

Chapter 8: Review of Previous Empirical Comparisons

Chapter 9: Dataset Descriptions and Results

Chapter 10: Analysis of Results

Chapter 11: Conclusions

Chapter 12: Knowledge Representation

Chapter 13: Learning to Control Dynamical Systems

Appendices, References and Index