Pattern Recognition and Neural Networks

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Cambridge University Press, Jan 18, 1996 - Computers - 403 pages
4 Reviews
This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author's website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.
 

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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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