num_pages: 653
description: 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.
language: English
title: A Probabilistic Theory of Pattern Recognition
titlecomplete: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
genres: Textbooks
ratings_count: 12
publisher: Springer