A Probabilistic Theory of Pattern Recognition

by Luc Devroye, László Györfi, Gábor Lugosi

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.

Genres: Textbooks, Mathematics

653 pages, Hardcover

First published February 20, 1996

Reviews

[Richard Zhu · Rating 4 out of 5]

> Pattern recognition is thus easier than regression function estimation.

Worth reading for this section alone

[Latarsha · Rating 3 out of 5]

Balance of Content The balance between dialogue, description, and action is maintained quite well. No single element dominates the text, which creates a harmonious flow for the reader. The author moves between these different modes of writing with professional ease. Discover the ratio of action to dialogue by following the link. >>> https://script.google.com/macros/s/AK...