Statistical learning theory and stochastic optimization
Ecole d'été de probabilités de Saint-Flour (31st 2001)
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Statistical learning theory and stochastic optimization
by Ecole d'été de probabilités de Saint-Flour (31st 2001)
Published
2004
Publisher
Springer
Pages
272
ISBN-10
3540225722
Description
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.
Subjects
Frequently Asked Questions
How many pages are in Statistical learning theory and stochastic optimization?
This edition of Statistical learning theory and stochastic optimization has approximately 272 pages. Please note, this is an estimate and the exact page count can vary between hardcover, paperback, and e-book versions.
How long does it take to read Statistical learning theory and stochastic optimization?
For most readers, Statistical learning theory and stochastic optimization typically takes between 5h 40m and 3h 47m to complete. This is based on the book's length of approximately 68,000 words and common reading speeds.
Here's a detailed breakdown: • Continuous reading at 250 WPM: approximately 4h 32m of focused reading • Casual reading (30 minutes/day): you could finish in roughly 10 days • Estimated word count: 68,000 words
Your individual reading time will vary based on your personal reading pace, the amount of daily reading time, and your familiarity with the subject matter.
What is the word count of Statistical learning theory and stochastic optimization?
The estimated word count for Statistical learning theory and stochastic optimization is approximately 68,000 words. This figure is calculated using industry-standard methods that consider genre-specific word density patterns, typical formatting and layout characteristics, and standard words-per-page ratios for published books.
This is an approximation — actual word count may vary based on font size, formatting, edition, and the presence of illustrations or charts.
Who is the author of Statistical learning theory and stochastic optimization?
Statistical learning theory and stochastic optimization was written by Ecole d'été de probabilités de Saint-Flour (31st 2001).
When was Statistical learning theory and stochastic optimization published?
The publication date for this specific edition is 2004. The original work may have been published on a different date.