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A Probabilistic Theory of Pattern Recognition Devroye Gyorfi Lugosi Like New HC

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Item specifics

Condition
Like New: A book that looks new but has been read. Cover has no visible wear, and the dust jacket ...
Book Title
A Probabilistic Theory of Pattern Recognition
ISBN
9780387946184
Publication Name
Probabilistic Theory of Pattern Recognition
Item Length
9.3in
Publisher
Springer New York
Publication Year
1997
Series
Stochastic Modelling and Applied Probability Ser.
Type
Textbook
Format
Hardcover
Language
English
Item Height
0.6in
Author
László Györfi, Gabor Lugosi, Luc Devroye
Item Width
6.1in
Item Weight
85.4 Oz
Number of Pages
Xv, 638 Pages

About this product

Product Information

Pattern recognition presents a significant challege for scientists and engineers, and many different approaches have been proposed. This book provides a self-contained account of probabilistic techniques that have been applied to the subject. Researchers and graduate students will benefit from this wide-ranging account of the field.

Product Identifiers

Publisher
Springer New York
ISBN-10
0387946187
ISBN-13
9780387946184
eBay Product ID (ePID)
203797

Product Key Features

Author
László Györfi, Gabor Lugosi, Luc Devroye
Publication Name
Probabilistic Theory of Pattern Recognition
Format
Hardcover
Language
English
Publication Year
1997
Series
Stochastic Modelling and Applied Probability Ser.
Type
Textbook
Number of Pages
Xv, 638 Pages

Dimensions

Item Length
9.3in
Item Height
0.6in
Item Width
6.1in
Item Weight
85.4 Oz

Additional Product Features

Series Volume Number
31
Number of Volumes
1 Vol.
Lc Classification Number
Qa273.A1-274.9
Table of Content
Preface * Introduction * The Bayes Error * Inequalities and alternate distance measures * Linear discrimination * Nearest neighbor rules * Consistency * Slow rates of convergence Error estimation * The regular histogram rule * Kernel rules Consistency of the k-nearest neighbor rule * Vapnik-Chervonenkis theory * Combinatorial aspects of Vapnik- Chervonenkis theory * Lower bounds for empirical classifier selection * The maximum likelihood principle * Parametric classification * Generalized linear discrimination * Complexity regularization * Condensed and edited nearest neighbor rules * Tree classifiers * Data- dependent partitioning * Splitting the data * The resubstitution estimate * Deleted estimates of the error probability * Automatic kernel rules * Automatic nearest neighbor rules * Hypercubes and discrete spaces * Epsilon entropy and totally bounded sets * Uniform laws of large numbers * Neural networks * Other error estimates * Feature extraction * Appendix * Notation * References * Index
Copyright Date
1996
Topic
Probability & Statistics / General, Cognitive Psychology & Cognition, Computer Vision & Pattern Recognition
Lccn
95-044633
Dewey Decimal
003/.52/015192
Intended Audience
Scholarly & Professional
Dewey Edition
20
Illustrated
Yes
Genre
Computers, Psychology, Mathematics

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  • Great for research in classification

    A well written text on classification and pattern recognition. I am in an applied mathematics masters program, so my prerequisites in measure theoretic probability were very weak. However, I feel like the book was very self contained, with an appendix to cover all of the essential concepts in probability. The proofs are well written (albeit way beyond my level) and thorough. Covers many different methods of classification.