Pattern Recognition and Neural Networks

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.

Brian D. Ripley (Author)

9780521717700, Cambridge University Press

Paperback, published 10 January 2008

416 pages, 41 b/w illus.
24.4 x 18.9 x 1.9 cm, 0.88 kg

'… a valuable reference for engineers and science researchers.' Optics and Photonics News

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.

1. Introduction and examples
2. Statistical decision theory
3. Linear discriminant analysis
4. Flexible discriminants
5. Feed-forward neural networks
6. Non-parametric methods
7. Tree-structured classifiers
8. Belief networks
9. Unsupervised methods
10. Finding good pattern features
Appendix: statistical sidelines
Glossary
References
Author index
Subject index.

Subject Areas: Pattern recognition [UYQP], Neural networks & fuzzy systems [UYQN], Probability & statistics [PBT]