A Probabilistic Theory of Pattern Recognition: Stochastic Modelling and Applied Probability, cartea 31
Autor Luc Devroye, Laszlo Györfi, Gabor Lugosien Limba Engleză Hardback – 4 apr 1996
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Specificații
ISBN-13: 9780387946184
ISBN-10: 0387946187
Pagini: 638
Ilustrații: XV, 638 p.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 1.1 kg
Ediția:1996
Editura: Springer
Colecția Springer
Seria Stochastic Modelling and Applied Probability
Locul publicării:New York, NY, United States
ISBN-10: 0387946187
Pagini: 638
Ilustrații: XV, 638 p.
Dimensiuni: 155 x 235 x 41 mm
Greutate: 1.1 kg
Ediția:1996
Editura: Springer
Colecția Springer
Seria Stochastic Modelling and Applied Probability
Locul publicării:New York, NY, United States
Public țintă
ResearchCuprins
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
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