Vai al contenuto principale della pagina
Autore: |
Devroye Luc
![]() |
Titolo: |
A Probabilistic Theory of Pattern Recognition / / by Luc Devroye, Laszlo Györfi, Gabor Lugosi
![]() |
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 | |
Probability Theory and Stochastic Processes | |
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.: | 9910819874703321 |
Lo trovi qui: | Univ. Federico II |
Opac: | Controlla la disponibilità qui |