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[null,"1.","Record Nr.",null,"UNISA996466497103316"]
[null,null,"Autore",null,"Catoni Olivier"]
[null,null,"Titolo",null,"Statistical Learning Theory and Stochastic Optimization [[electronic resource] ] : Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001 / / by Olivier Catoni ; edited by Jean Picard"]
[null,null,"Pubbl/distr/stampa",null,"Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2004"]
[null,null,"ISBN",null,"3-540-44507-2"]
[null,null,"Edizione",null,"[1st ed. 2004.]"]
[null,null,"Descrizione fisica",null,"1 online resource (VIII, 284 p.)"]
[null,null,"Collana",null,"École d'Été de Probabilités de Saint-Flour, , 0721-5363 ; ; 1851"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Probabilities"]
[{"$nulls":4},"Statistics "]
[{"$nulls":4},"Mathematical optimization"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Information theory"]
[{"$nulls":4},"Numerical analysis"]
[{"$nulls":4},"Probability Theory and Stochastic Processes"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Optimization"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Information and Communication, Circuits"]
[{"$nulls":4},"Numerical Analysis"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Universal Lossless Data Compression -- Links Between Data Compression and Statistical Estimation -- Non Cumulated Mean Risk -- Gibbs Estimators -- Randomized Estimators and Empirical Complexity -- Deviation Inequalities -- Markov Chains with Exponential Transitions -- References -- Index."]
[null,null,null,"Sommario/riassunto","Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously \"wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-"]
[{"$nulls":4},"Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results."]