Pattern Recognition and Neural Networks

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Cambridge University Press, Jan 18, 1996 - Computers - 403 pages
4 Reviews
Pattern recognition has long been studied in relation to many different (and mainly unrelated) applications, such as remote sensing, computer vision, space research, and medical imaging. In this book Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks. Unifying principles are brought to the fore, and the author gives an overview of the state of the subject. Many examples are included to illustrate real problems in pattern recognition and how to overcome them.This is a self-contained account, ideal both as an introduction for non-specialists readers, and also as a handbook for the more expert reader.
 

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Contents

Introduction and Examples
3
Statistical Decision Theory
17
Linear Discriminant Analysis
91
Flexible Discriminants
121
Feedforward Neural Networks
143
Nonparametric Methods
181
Treestructured Classifiers
213
Belief Networks
243
Unsupervised Methods
287
Finding Good Pattern Features
327
A Statistical Sidelines
333
Glossary
347
References
355
Author Index
391
Subject Index
399
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About the author (1996)

Brian Ripley is the Professor of Applied Statistics at the University of Oxford and a member of the Department of Statistics as well as a Professorial Fellow of St. Peter's College.

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