Statistical Learning Theory and Stochastic Optimization: Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001 - Couverture souple

Catoni, Olivier

Couverture souple

ISBN 10 : 3662203243 ISBN 13 : 9783662203248

Editeur : Springer, 2014

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: 2014
• ISBN 10: 3662203243
• ISBN 13: 9783662203248
• Reliure: Broché
• Langue: anglais
• Coordonnées du fabricant: non disponible