The Resource A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi

A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi

Label
A probabilistic theory of pattern recognition
Title
A probabilistic theory of pattern recognition
Statement of responsibility
Luc Devroye, László Györfi, Gábor Lugosi
Creator
Contributor
Subject
Language
eng
Summary
  • Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, tree classifiers, and neural networks
  • Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material
Member of
Cataloging source
DLC
http://library.link/vocab/creatorName
Devroye, Luc
Illustrations
illustrations
Index
index present
LC call number
Q327
LC item number
.D5 1996
Literary form
non fiction
Nature of contents
bibliography
http://library.link/vocab/relatedWorkOrContributorName
  • Györfi, László
  • Lugosi, Gábor
http://library.link/vocab/subjectName
  • Pattern perception
  • Probabilities
  • arbre classification
  • estimation erreur
  • plus proche voisin
  • analyse discriminante
  • reconnaissance forme
  • théorie probabilité
  • Perception des structures
  • Probabilités
  • Pattern perception
  • Probabilities
  • Patroonherkenning
  • Waarschijnlijkheidstheorie
  • Reconhecimento de padrões
  • Inteligência artificial
  • Perception des structures
  • Probabilités
  • Wahrscheinlichkeitstheorie
  • Mustererkennung
Label
A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi
Instantiates
Publication
Bibliography note
Includes bibliographical references (p. [593]-618) and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
24 cm.
Extent
xv, 636 pages
Isbn
9780387946184
Lccn
95044633
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)33276839
  • (OCoLC)ocm33276839
Label
A probabilistic theory of pattern recognition, Luc Devroye, László Györfi, Gábor Lugosi
Publication
Bibliography note
Includes bibliographical references (p. [593]-618) and indexes
Carrier category
volume
Carrier category code
  • nc
Carrier MARC source
rdacarrier
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
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
Dimensions
24 cm.
Extent
xv, 636 pages
Isbn
9780387946184
Lccn
95044633
Media category
unmediated
Media MARC source
rdamedia
Media type code
  • n
Other physical details
illustrations
System control number
  • (OCoLC)33276839
  • (OCoLC)ocm33276839

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