Statistical Pattern Recognition

By Andrew R. Webb and Keith D. Copsey

Book Overview

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems. * Provides a self-contained introduction to statistical pattern recognition. * Each technique described is illustrated by real examples. * Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification. * Each section concludes with a description of the applications that have been addressed and with further developments of the theory. * Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability. * Features a variety of exercises, from 'open-book' questions to more lengthy projects. The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments. For further information on the techniques and applications discussed in this book please visit www.statistical-pattern-recognition.net

• Format: Paperback
• Language: English
• ISBN: 0470845147
• ISBN13: 9780470845141
• Release Date: September 2002
• Publisher: Wiley
• Length: 514 Pages
• Weight: 0.55 lbs.
• Dimensions: 9.7" x 1.1" x 6.8"

Customer Reviews

The most comprehensive book about machine learning

The book written by Andrew Webb is certainly the most comprehensive book related to machine learning. I have not been able to find any machine learning topic which is not treated in this book. According to me, this book is more for a scientific audience for the simplest reason that the presentation gives more importance to equations than to application examples. It does not explain how to program machine learning algorithm but rather which algorithms exist and what is their mathematical background. Every technique is presented first using text and only then mathematical development is shown. Therefore, it is convenient for people preferring textual description as well as the ones preferring equations. The book is very well structured. Every chapter starts with a textual introduction on the related issue and then describes several techniques to solve it. At the end, specific application examples are given. A large part is then devoted to summary, discussion, recommendations (not always), notes and references, and finally exercises. Topics are covered in a non standard way for people used to data mining practical books. After an introduction, density estimation techniques are explained. Then linear and non-linear discriminant analyzes. It goes on with decision trees, performance and feature selection to finish with clustering and some other additional topics. Although this book is written in a statistical point of view, it is certainly one of the most comprehensive resource for machine learning and data mining.

This book is good guidance.

I recently started study about Pattern Recognition. This book is so well organized. - Introduction to statistical pattern recognition- Basic approaches to supervised classification via Bayes' rule and estimation of the class-conditional densities.- Discriminant function approach to supervised classification.- Techniques of exploratory data analysis.- Additional topics on pattern recognition including performance assessment.Especially, this book contains URL which concerned with topics. It is very useful!!