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

Catoni, Olivier

Softcover

ISBN 10: 3662203243 ISBN 13: 9783662203248

Verlag: Springer, 2014

Inhaltsangabe

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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2014
• Sprache: Englisch
• ISBN 10: 3662203243
• ISBN 13: 9783662203248
• Einband: Tapa blanda
• Anzahl der Seiten: 292
• Herausgeber: Picard Jean
• Kontakt zum Hersteller: ROXIO; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; 22391; Deutschland