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Statistical Learning Theory and Stochastic Optimization: Ecole d'Et?? de Probabilit??s de Saint-Flour XXXI - 2001: 1851 (Lecture Notes in Mathematics, 1851) - Tapa blanda

Catoni, Olivier; Picard, Jean

 
9783540225720: Statistical Learning Theory and Stochastic Optimization: Ecole d'Et?? de Probabilit??s de Saint-Flour XXXI - 2001: 1851 (Lecture Notes in Mathematics, 1851)
Tapa blanda
ISBN 10: 3540225722 ISBN 13: 9783540225720
Editorial: Springer, 2008

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

De la contraportada

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.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2008
Idioma
Inglés
ISBN 10 
3540225722
ISBN 13 
9783540225720
Encuadernación
Tapa blanda
Número de páginas
292
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