04303nam 22006375 450 991081987470332120240516011031.01-4612-0711-810.1007/978-1-4612-0711-5(CKB)3390000000040618(SSID)ssj0001067857(PQKBManifestationID)11600980(PQKBTitleCode)TC0001067857(PQKBWorkID)11093852(PQKB)10184058(DE-He213)978-1-4612-0711-5(MiAaPQ)EBC3074637(PPN)238006980(EXLCZ)99339000000004061820131120d1996 u| 0engurnn|008mamaatxtccrA Probabilistic Theory of Pattern Recognition /by Luc Devroye, Laszlo Györfi, Gabor Lugosi1st ed. 1996.New York, NY :Springer New York :Imprint: Springer,1996.1 online resource (XV, 638 p.) Stochastic Modelling and Applied Probability,0172-4568 ;31Bibliographic Level Mode of Issuance: Monograph0-387-94618-7 1-4612-6877-X Includes bibliographical references and indexes.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.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.Stochastic Modelling and Applied Probability,0172-4568 ;31ProbabilitiesPattern recognitionProbability Theory and Stochastic Processeshttps://scigraph.springernature.com/ontologies/product-market-codes/M27004Pattern Recognitionhttps://scigraph.springernature.com/ontologies/product-market-codes/I2203XProbabilities.Pattern recognition.Probability Theory and Stochastic Processes.Pattern Recognition.519.2Devroye Lucauthttp://id.loc.gov/vocabulary/relators/aut60985Györfi Laszloauthttp://id.loc.gov/vocabulary/relators/autLugosi Gaborauthttp://id.loc.gov/vocabulary/relators/autMiAaPQMiAaPQMiAaPQBOOK9910819874703321A Probabilistic Theory of Pattern Recognition3970911UNINA