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Record Nr. |
UNINA9910819874703321 |
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Autore |
Devroye Luc |
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Titolo |
A Probabilistic Theory of Pattern Recognition / / by Luc Devroye, Laszlo Györfi, Gabor Lugosi |
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Pubbl/distr/stampa |
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New York, NY : , : Springer New York : , : Imprint : Springer, , 1996 |
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ISBN |
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Edizione |
[1st ed. 1996.] |
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Descrizione fisica |
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1 online resource (XV, 638 p.) |
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Collana |
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Stochastic Modelling and Applied Probability, , 0172-4568 ; ; 31 |
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Disciplina |
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Soggetti |
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Probabilities |
Pattern recognition |
Probability Theory and Stochastic Processes |
Pattern Recognition |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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. |
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