Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
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USDRipley 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.
- The most reliable account of the subject available - now in paperback
- Unparalleled coverage with valuable insights into the theory and a wide range of applications
- Real case-studies, data sets and examples help build skills and understanding
Reviews & endorsements
"...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
Product details
January 2008Paperback
9780521717700
416 pages
244 × 189 × 19 mm
0.88kg
41 b/w illus.
Available
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.
