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[null,"1.","Record Nr.",null,"UNINA990004908120403321"]
[null,null,"Titolo",null,"A brief sketch of the Morris Movement : and of the firm founded by William Morris ... Written to commemorate the firm's fiftieth anniversary in june 1911"]
[null,null,"Pubbl/distr/stampa",null,"London : Privately Printed for Morris & Co., 1911"]
[null,null,"Descrizione fisica",null,"63 p., [9] tav. : ill. ; 15 cm"]
[null,null,"Disciplina",null,"745.2"]
[null,null,"Locazione",null,"FLFBC"]
[null,null,"Collocazione",null,"745.2 MOR 1"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910959371503321"]
[null,null,"Autore",null,"Devroye Luc"]
[null,null,"Titolo",null,"A Probabilistic Theory of Pattern Recognition / / by Luc Devroye, Laszlo Györfi, Gabor Lugosi"]
[null,null,"Pubbl/distr/stampa",null,"New York, NY : , : Springer New York : , : Imprint : Springer, , 1996"]
[null,null,"ISBN",null,"1-4612-0711-8"]
[null,null,"Edizione",null,"[1st ed. 1996.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XV, 638 p.)"]
[null,null,"Collana",null,"Stochastic Modelling and Applied Probability, , 2197-439X ; ; 31"]
[null,null,"Disciplina",null,"519.2"]
[null,null,"Soggetti",null,"Probabilities"]
[{"$nulls":4},"Pattern recognition systems"]
[{"$nulls":4},"Probability Theory"]
[{"$nulls":4},"Automated Pattern Recognition"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and indexes."]
[null,null,"Nota di contenuto",null,"A Probabilistic Theory of Pattern Recognition -- Editor's page -- A Probabilistic Theory of Pattern Recognition -- Copyright -- Preface -- Contents -- 1 Introduction -- 2 The Bayes Error -- 3 Inequalities and Alternate Distance Measures -- 4 Linear Discrimination -- 5 Nearest Neighbor Rules -- 6 Consistency -- 7 Slow Rates of Convergence -- 8 Error Estimation -- 9 The Regular Histogram Rule -- 10 Kernel Rules -- 11 Consistency of the k-Nearest Neighbor Rule -- 12 Vapnik -Chervonenkis Theory -- 13 Combinatorial Aspects of Vapnik -Chervonenkis Theory -- 14 Lower Bounds for Empirical Classifier Selection -- 15 The Maximum Likelihood Principle -- 16 Parametric Classification -- 17 Generalized Linear Discrimination -- 18 Complexity Regularization -- 19 Condensed and Edited Nearest Neighbor Rules -- 20 Tree Classifiers -- 21 Data- Dependent Partitioning -- 22 Splitting the Data -- 23 The Resubstitution Estimate -- 24 Deleted Estimates of the Error Probability -- 25 Automatic Kernel Rules -- 26 Automatic Nearest Neighbor Rules -- 27 Hypercubes and Discrete Spaces -- 28 Epsilon Entropy and Totally Bounded Sets -- 29 Uniform Laws of Large Numbers -- 30 Neural Networks -- 31 Other Error Estimates -- 32 Feature Extraction -- Appendix -- Notation -- References -- Author Index -- Subject Index."]
[null,null,null,"Sommario/riassunto","Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account "]
[{"$nulls":4},"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."]