A Probabilistic Theory of Pattern Recognition

A Probabilistic Theory of Pattern Recognition
Author: Luc Devroye
Publisher: Springer Science & Business Media
Total Pages: 658
Release: 1997-02-20
Genre: Mathematics
ISBN: 0387946187


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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.


A Probabilistic Theory of Pattern Recognition
Language: en
Pages: 631
Authors: Luc Devroye
Categories: Mathematics
Type: BOOK - Published: 2013-11-27 - Publisher: Springer Science & Business Media

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A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theor
A Probabilistic Theory of Pattern Recognition
Language: en
Pages: 660
Authors: Luc Devroye
Categories:
Type: BOOK - Published: 2014-09-01 - Publisher:

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Pattern Recognition and Machine Learning
Language: en
Pages: 0
Authors: Christopher M. Bishop
Categories: Computers
Type: BOOK - Published: 2016-08-23 - Publisher: Springer

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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approxi
Machine Learning
Language: en
Pages: 1102
Authors: Kevin P. Murphy
Categories: Computers
Type: BOOK - Published: 2012-08-24 - Publisher: MIT Press

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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic d
Principles of Nonparametric Learning
Language: en
Pages: 344
Authors: Laszlo Györfi
Categories: Technology & Engineering
Type: BOOK - Published: 2014-05-04 - Publisher: Springer

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This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and esti