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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems - Vladimir Koltchinskii
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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d?Été de Probabilités de Saint-Flour XXXVIII-2008 (Lecture Notes in Mathematics, Band 2033) - Koltchinskii, Vladimir
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ISBN: 9783642221460

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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d?Été de Probabilités de Saint-Flour XXXVIII-2008 (Lecture Notes in Mathematics, Band 2033) - Koltchinskii, Vladimir
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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d?Été de Probabilités de Saint-Flour XXXVIII-2008 (Lecture Notes in Mathematics, Band 2033) - Livres de poche

2011, ISBN: 9783642221460

Springer, Taschenbuch, Auflage: 2011, 268 Seiten, Publiziert: 2011-07-29T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: biography, 0.84 kg, Verkaufsrang: 5029738, Statistik, Naturwissens… Plus…

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2011, ISBN: 3642221467

2011 Kartoniert / Broschiert Wahrscheinlichkeit - Wahrscheinlichkeitstheorie, Wahrscheinlichkeitsrechnung und Statistik, 62J99,62H12,60B20,60G99; concentrationinequalities; empiricalpro… Plus…

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Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d?Été de Probabilités de Saint-Flour XXXVIII-2008 (Lecture Notes in Mathematics, Band 2033)
Auteur:
Titre:
ISBN:

3642221467


The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.

Informations détaillées sur le livre - Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems: École d?Été de Probabilités de Saint-Flour XXXVIII-2008 (Lecture Notes in Mathematics, Band 2033)


EAN (ISBN-13): 9783642221460
ISBN (ISBN-10): 3642221467
Version reliée
Livre de poche
Date de parution: 2011
Editeur: Springer
254 Pages
Poids: 0,399 kg
Langue: Englisch

Livre dans la base de données depuis 2007-04-25T11:35:53+02:00 (Paris)
Page de détail modifiée en dernier sur 2024-01-11T08:27:31+01:00 (Paris)
ISBN/EAN: 3642221467

ISBN - Autres types d'écriture:
3-642-22146-7, 978-3-642-22146-0
Autres types d'écriture et termes associés:
Auteur du livre: erl vladimir, rademacher, norm
Titre du livre: oracle, ecole ete probabilites saint flour, minim, probabilités, 2033, recovery, lecture notes mathematics, inequalities

Données de l'éditeur

Auteur: Vladimir Koltchinskii
Titre: Lecture Notes in Mathematics; École d'Été de Probabilités de Saint-Flour; Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems - École d’Été de Probabilités de Saint-Flour XXXVIII-2008
Editeur: Springer; Springer Berlin
254 Pages
Date de parution: 2011-07-29
Berlin; Heidelberg; DE
Imprimé / Fabriqué en
Langue: Anglais
80,24 € (DE)
82,49 € (AT)
88,50 CHF (CH)
POD
IX, 254 p.

BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Verstehen; Mathematik; 62J99, 62H12, 60B20, 60G99; concentration inequalities; empirical processes; low rank matrix recovery; sparse recovery; Probability Theory; Stochastik; EA

The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.
Provides a unified framework for machine learning problems (such as large margin classification), sparse recovery and low rank matrix problems Develops a variety of probabilistic inequalities for empirical processes needed to obtain error bounds in machine learning and sparse recovery Develops a comprehensive theory of excess risk bounds and oracle inequalities for penalized empirical risk minimization Includes supplementary material: sn.pub/extras

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