Statistical learning theory and stochastic optimization - Info and Reading Options
Ecole d'eté de probabilités de Saint-Flour XXXI, 2001
By Ecole d'été de probabilités de Saint-Flour (31st 2001)
"Statistical learning theory and stochastic optimization" was published by Springer in 2004 - Berlin, it has 272 pages and the language of the book is English.
“Statistical learning theory and stochastic optimization” Metadata:
- Title: ➤ Statistical learning theory and stochastic optimization
- Author: ➤ Ecole d'été de probabilités de Saint-Flour (31st 2001)
- Language: English
- Number of Pages: 272
- Publisher: Springer
- Publish Date: 2004
- Publish Location: Berlin
“Statistical learning theory and stochastic optimization” Subjects and Themes:
- Subjects: ➤ Congresses - Mathematical statistics - Probabilities - Statistics - Mathematical optimization - Stochastic processes - Probabilités - Congrès - Statistique mathématique - Statistique - Statistiek - Stochastische methoden - Optimaliseren - Artificial intelligence - Mathematics - Numerical analysis - Distribution (Probability theory)
Edition Specifications:
- Pagination: viii, 272 p. :
Edition Identifiers:
- The Open Library ID: OL3315365M - OL5743041W
- Online Computer Library Center (OCLC) ID: 56714791
- Library of Congress Control Number (LCCN): 2004109143
- ISBN-10: 3540225722
- All ISBNs: 3540225722
AI-generated Review of “Statistical learning theory and stochastic optimization”:
"Statistical learning theory and stochastic optimization" Description:
The Open Library:
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.
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