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Statistical learning theory and stochastic optimization : Ecole d'Eté de Probabilités de Saint-Flour XXXI-2001

Auteur : Olivier Catoni; Jean Picard; LINK (Online service)
Éditeur : Berlin : Springer-Verlag, ©2004.
Collection : Lecture notes in mathematics (Springer-Verlag), 1851.
Édition/format :   Livre électronique : Document : Publication de conférence : AnglaisVoir toutes les éditions et tous les formats
Base de données :WorldCat
Résumé :
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  Lire la suite...
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Genre/forme : Electronic books
Conference proceedings
Congresses
Format – détails additionnels : Print version:
Catoni, Olivier.
Statistical learning theory and stochastic optimization.
Berlin : Springer-Verlag, ©2004
(DLC) 2004109143
(OCoLC)56714791
Type d’ouvrage : Publication de conférence, Document, Ressource Internet
Format : Ressource Internet, Fichier informatique
Tous les auteurs / collaborateurs : Olivier Catoni; Jean Picard; LINK (Online service)
ISBN : 9783540445074 3540445072
Numéro OCLC : 56508135
Notes : " ... 31st Probability Summer School in Saint-Flour (July 8-25, 2001) ..."--Preface.
Description : 1 online resource (viii, 272 pages) : illustrations.
Contenu : 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.
Titre de collection : Lecture notes in mathematics (Springer-Verlag), 1851.
Autres titres : Ecole d'Eté de Probabilités de Saint-Flour XXXI-2001
Responsabilité : Olivier Catoni ; editor, Jean Picard.

Résumé :

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-Bayesian theorems. Results on the  Lire la suite...

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