TY - ELEC
DB - /z-wcorg/
DP - http://worldcat.org
ID - 56508135
LA - English
UR - http://rave.ohiolink.edu/ebooks/ebc/11305972
T1 - Statistical learning theory and stochastic optimization Ecole d'Eté de Probabilités de Saint-Flour XXXI-2001
AU - Catoni, Olivier.
AU - Picard, Jean.
AU - LINK (Online service)
AU - Ecole d'été de probabilités de Saint-Flour
PB - Springer-Verlag
CY - Berlin
Y1 - 2004///
SN - 9783540445074 3540445072
AB - 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.
ER -