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A title with a broad appeal, including biologists, astronomers, and statisticians.
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.
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.
'The combination of theory and examples makes this a unique and interesting book.' A. Gelman, Journal of the International Statistical Institute 'I can warmly recommend this book. Every researcher will benefit by the broadness of Ripley's view and the comprehensive bibliography.' Dee Denteneer, ITW Nieuws '! a grand overview of both the theory and the practice of the field ! of benefit to anyone who has an interest in a principled approach to statistical data analysis ! will indeed provide an excellent reference for many years to come.' Stephen Roberts, The Times Higher Educational Supplement '... 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 and Photonics News