A Probabilistic Theory of Pattern Recognition
(1st ed. 1996. Corr. 2nd printing 1997)
Part of the Stochastic Modelling and Applied Probability series
A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction.
Each chapter concludes with problems and exercises to further the readers understanding.
Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.