LEADER 04303nam 22006375 450 001 9910819874703321 005 20240516011031.0 010 $a1-4612-0711-8 024 7 $a10.1007/978-1-4612-0711-5 035 $a(CKB)3390000000040618 035 $a(SSID)ssj0001067857 035 $a(PQKBManifestationID)11600980 035 $a(PQKBTitleCode)TC0001067857 035 $a(PQKBWorkID)11093852 035 $a(PQKB)10184058 035 $a(DE-He213)978-1-4612-0711-5 035 $a(MiAaPQ)EBC3074637 035 $a(PPN)238006980 035 $a(EXLCZ)993390000000040618 100 $a20131120d1996 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 12$aA Probabilistic Theory of Pattern Recognition /$fby Luc Devroye, Laszlo Györfi, Gabor Lugosi 205 $a1st ed. 1996. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d1996. 215 $a1 online resource (XV, 638 p.) 225 1 $aStochastic Modelling and Applied Probability,$x0172-4568 ;$v31 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-387-94618-7 311 $a1-4612-6877-X 320 $aIncludes bibliographical references and indexes. 327 $aA 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. 330 $aPattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance measures, nonparametric methods based on kernels or nearest neighbors, Vapnik-Chervonenkis theory, epsilon entropy, parametric classification, error estimation, free classifiers, and neural networks. Wherever possible, distribution-free properties and inequalities are derived. A substantial portion of the results or the analysis is new. Over 430 problems and exercises complement the material. 410 0$aStochastic Modelling and Applied Probability,$x0172-4568 ;$v31 606 $aProbabilities 606 $aPattern recognition 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aProbabilities. 615 0$aPattern recognition. 615 14$aProbability Theory and Stochastic Processes. 615 24$aPattern Recognition. 676 $a519.2 700 $aDevroye$b Luc$4aut$4http://id.loc.gov/vocabulary/relators/aut$060985 702 $aGyörfi$b Laszlo$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aLugosi$b Gabor$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819874703321 996 $aA Probabilistic Theory of Pattern Recognition$93970911 997 $aUNINA