A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)

Luc Devroye

ISBN 10: 146126877X / ISBN 13: 9781461268772

Editore: Springer 2013-11-22, 2013

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Codice articolo 6666-IUK-9781461268772

Dati bibliografici

• Titolo: A Probabilistic Theory of Pattern Recognition (Stochastic Modelling and Applied Probability)
• Editore: Springer 2013-11-22
• Data di pubblicazione: 2013
• Lingua: Inglese
• ISBN 10: 146126877X
• ISBN 13: 9781461268772
• Rilegatura: Paperback
• Condizione: New

Descrizione articolo

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.

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