No subject

sciences, 'Statistical Pattern Recognition' shows how closely these fields
are related in terms of application. Areas such as database design,
artificial neural networks and decision support are common to all. The
author also examines the more diverse theoretical topics available to the
practitioner or researcher, such as outlier detection and model selection,
and concludes each section with a description of the wider range of
practical applications and the future developments of theoretical
techniques.

Providing an introduction to statistical pattern theory and techniques that
draws on material from a wide range of fields, 'Statistical Pattern
Recognition' is a must for all technical professionals wanting to get up to
speed on the recent advances in this dynamic subject area.


Key Features

Contains descriptions of the most up-to-date pattern processing techniques,
including the recent advances in non-parametric approaches to discrimination

Illustrates the techniques with examples of real-world applications studies

Includes a variety of exercises from 'open-book' questions to more lengthy
projects


Reviews

'... features a 'how to' approach with examples and exercises'
			Lavoiser

Contents: Introduction to statistical pattern recognition / Estimation /
Density estimation / Linear discriminant analysis / Non-linear discriminant
analysis - neural networks / Non-linear discriminant analysis - statistical
methods / Classification trees / Feature selection and extraction /
Clustering / Additional topics / Measures of dissimilarity / Parameter
estimation / Linear algebra / Data / Probability theory.


1999	   480pp     Paperback     ISBN   0 340 74164 3      29.99