返回信息流Pattern Recognition and Neural Networks
Brian D. Ripley
University of Oxford
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.
Reviews
"...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 Journa
http://www.stats.ox.ac.uk/~ripley/PRbook/
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