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.
From the Back Cover
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.
For ease of access, the book has been cut into chapters, each one
downloadable as a (gzipped) postcript file.
You can donwload the files with (shift-left mouse button) or, if you have a postcript previewer, simply click on the link. Windows users may prefer zipped
files which are available here.
If you have problems with
these formats, please let me know.
The Whole Book (gzipped 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
Maintained by: charles@maths.leeds.ac.uk
This page was last updated on 16 April 1999.