Statistical Learning Theory and Stochastic Optimization : Ecole d Eté de Probabilités de Saint-Flour XXXI - 2001
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 p...
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| Wydane w: | Ecole d'Eté de Probabilités de Saint-Flour (Online), 31 |
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| Format: | Livre numérique |
| Język: | Anglais |
| Wydane: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| Seria: | Lecture notes in mathematics
1851 |
| Hasła przedmiotowe: | |
| Dostęp online: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
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N˚de : École d'Été de probabilités de Saint-Flour, ISSN 2512-3564, XXXI, 2001 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Consulter le titre dans une bibliothèque: | Cliquer ici |
| Edition sous un autre format: | • Statistical learning theory and stochastic optimization, Ecole d'été de probabilités de Saint-Flour XXXI-2001, [course presented by] Olivier Catoni, 2004, Berlin, Springer, 1 volume (viii-272 pages), Lecture notes in mathematics, 3-540-22572-2 • Statistical Learning Theory and Stochastic Optimization, Texte imprimé, 9783662203248 |
| Streszczenie: | 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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| Deskrypcja: | N˚de : École d'Été de probabilités de Saint-Flour, ISSN 2512-3564, XXXI, 2001 Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9783540445074 (PDF) |
| ISSN: | 1617-9692 2512-3564 |
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