Pattern Recognition and Neural Networks available in Paperback
- Pub. Date:
- Cambridge University Press
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.
|Publisher:||Cambridge University Press|
|Product dimensions:||7.40(w) x 9.60(h) x 0.70(d)|
About 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.
Table of Contents1. 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.