Statistical Learning Theory and Stochastic Optimization: Ecole d'Et?? de Probabilit??s de Saint-Flour XXXI - 2001 - Couverture souple

Catoni, Olivier; Picard, Jean

Couverture souple

ISBN 10 : 3540225722 ISBN 13 : 9783540225720

Editeur : Springer, 2008

Synopsis

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-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.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2008
• Langue: anglais
• ISBN 10: 3540225722
• ISBN 13: 9783540225720
• Reliure: Broché
• Nombre de pages: 292
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne