Pattern Recognition and Neural Networks

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Overview

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

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Editorial Reviews

From the Publisher
"...an excellent text on the statistics of pattern classifiers and the application of neural network techniques...Ripley has managed...to produce an altogether accessible text...[it] will be rightly popular with newcomers to the area for its ability to present the mathematics of statistical pattern recognition and neural networks in an accessible format and engaging style." Nature

"...a valuable reference for engineers and science researchers." Optics & Photonics News

"The combination of theory and examples makes this a unique and interesting book." International Statistical Institute Journal

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Product Details

  • ISBN-13: 9780521717700
  • Publisher: Cambridge University Press
  • Publication date: 1/28/2008
  • Pages: 416
  • Sales rank: 433,394
  • Product dimensions: 7.40 (w) x 9.60 (h) x 0.70 (d)

Meet the Author

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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Table of Contents

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.

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