A Probabilistic Theory of Pattern Recognition: 31 - Brossura

Devroye, Luc; Györfi, Laszlo; Lugosi, Gabor

Brossura

ISBN 10: 146126877X ISBN 13: 9781461268772

Casa editrice: Springer, 2013

Riassunto

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.

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Contenuti

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

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2013
• ISBN 10: 146126877X
• ISBN 13: 9781461268772
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 660
• Contatto del produttore: {language_tag:it_IT,value:"Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com"}