A Probabilistic Theory of Pattern Recognition / / by Luc Devroye, Laszlo Györfi, Gabor Lugosi

• Autore: Devroye Luc
• Pubblicazione: New York, NY : , : Springer New York : , : Imprint : Springer, , 1996
• Edizione: 1st ed. 1996.
• Descrizione fisica: 1 online resource (XV, 638 p.)
• Disciplina: 519.2
• Soggetto topico: Probabilities; Pattern recognition systems; Probability Theory; Automated Pattern Recognition
• Persona (resp. second.): GyörfiLaszlo; LugosiGabor
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: A Probabilistic Theory of Pattern Recognition -- Editor's page -- A Probabilistic Theory of Pattern Recognition -- Copyright -- Preface -- Contents -- 1 Introduction -- 2 The Bayes Error -- 3 Inequalities and Alternate Distance Measures -- 4 Linear Discrimination -- 5 Nearest Neighbor Rules -- 6 Consistency -- 7 Slow Rates of Convergence -- 8 Error Estimation -- 9 The Regular Histogram Rule -- 10 Kernel Rules -- 11 Consistency of the k-Nearest Neighbor Rule -- 12 Vapnik -Chervonenkis Theory -- 13 Combinatorial Aspects of Vapnik -Chervonenkis Theory -- 14 Lower Bounds for Empirical Classifier Selection -- 15 The Maximum Likelihood Principle -- 16 Parametric Classification -- 17 Generalized Linear Discrimination -- 18 Complexity Regularization -- 19 Condensed and Edited Nearest Neighbor Rules -- 20 Tree Classifiers -- 21 Data- Dependent Partitioning -- 22 Splitting the Data -- 23 The Resubstitution Estimate -- 24 Deleted Estimates of the Error Probability -- 25 Automatic Kernel Rules -- 26 Automatic Nearest Neighbor Rules -- 27 Hypercubes and Discrete Spaces -- 28 Epsilon Entropy and Totally Bounded Sets -- 29 Uniform Laws of Large Numbers -- 30 Neural Networks -- 31 Other Error Estimates -- 32 Feature Extraction -- Appendix -- Notation -- References -- Author Index -- Subject Index.
• Sommario/riassunto: 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, free 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.
• Titolo autorizzato: A Probabilistic Theory of Pattern Recognition
• ISBN: 1-4612-0711-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910959371503321
• Lo trovi qui: Univ. Federico II
• Serie: Stochastic Modelling and Applied Probability, . 2197-439X ; ; 31