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...
שמור ב:
| הוצא לאור ב: | Ecole d'Eté de Probabilités de Saint-Flour (Online), 31 |
|---|---|
| מחבר ראשי: | |
| מחבר תאגידי: | |
| מחברים אחרים: | |
| פורמט: | Livre numérique |
| שפה: | Anglais |
| יצא לאור: |
Berlin [etc.] :
Springer
[20..].
Cham : Springer Nature |
| סדרה: | Lecture notes in mathematics
1851 |
| נושאים: | |
| גישה מקוונת: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| הערה: |
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 |
| LEADER | 04149nam a22004577a 4500 | ||
|---|---|---|---|
| 001 | 969587 | ||
| 008 | 110927q2000 xxe ||| |||| 00| 0 eng d | ||
| 009 | PPN155176072 | ||
| 020 | |a 9783540445074 (PDF) | ||
| 041 | 0 | |a eng | |
| 082 | |a 310 | ||
| 082 | |a 519.2 | ||
| 100 | 1 | |a Catoni, Olivier, |d 1965-...., |c mathématicien. | |
| 245 | 1 | 0 | |a Statistical Learning Theory and Stochastic Optimization : |b Ecole d Eté de Probabilités de Saint-Flour XXXI - 2001 |c Olivier Catoni ; edited by Jean Picard. |
| 260 | |a Berlin [etc.] : |b Springer. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Lecture notes in mathematics |v 1851 |x 1617-9692 | |
| 500 | |a N˚de : École d'Été de probabilités de Saint-Flour, ISSN 2512-3564, XXXI, 2001 | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 500 | |a Archives Springer e-books (Licence nationale) | ||
| 505 | 0 | |a Universal Lossless Data Compression -- Links Between Data Compression and Statistical Estimation -- Non Cumulated Mean Risk -- Gibbs Estimators -- Randomized Estimators and Empirical Complexity -- Deviation Inequalities -- Markov Chains with Exponential Transitions -- References -- Index. | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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. | ||
| 650 | |a Probabilités | ||
| 650 | |a Optimisation mathématique | ||
| 650 | |a Intelligence artificielle | ||
| 650 | |a Analyse numérique | ||
| 650 | |a Méthodes statistiques | ||
| 650 | |a Actes de congrès | ||
| 700 | 1 | |a Picard, Jean, |d 1959-...., |c mathématicien. |4 pbd | |
| 711 | 2 | |a Ecole d'été de probabilités de Saint-Flour |n (31 |d :2001 |c :Saint-Flour, Cantal). |4 aut | |
| 773 | 0 | |0 162022972 |t Ecole d'Eté de Probabilités de Saint-Flour (Online) |x 2512-3564 |g 31 | |
| 776 | 0 | |0 080293670 |t Statistical learning theory and stochastic optimization |o Ecole d'été de probabilités de Saint-Flour XXXI-2001 |f [course presented by] Olivier Catoni |d 2004 |c Berlin |n Springer |p 1 volume (viii-272 pages) |s Lecture notes in mathematics |z 3-540-22572-2 | |
| 776 | 0 | |t Statistical Learning Theory and Stochastic Optimization |b Texte imprimé |z 9783662203248 | |
| 856 | 4 | |q PDF |u https://doi.org/10.1007/b99352 |z Accès sur la plateforme de l'éditeur | |
| 856 | 4 | |u https://revue-sommaire.istex.fr/ark:/67375/8Q1-PWKSM0SW-H |z Accès sur la plateforme Istex | |
| 856 | 4 | |5 452349901:750672455 |u https://ezproxy.univ-orleans.fr/login?url=https://doi.org/10.1007/b99352 |z Accès Université d'Orléans | |
| 856 | 4 | |5 180339901:754021149 |u https://ezproxy.insa-cvl.fr/login?qurl=https://doi.org/10.1007/b99352 |z Accès INSA CVL | |
| 997 | |0 969587 |1 Livre numérique |a Ressource numérique |b INSA |b ENSA |c 0/Bibliothèque numérique/ |c 1/Bibliothèque numérique/Autre ressource numérique/ | ||