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Fondamentaux de l'apprentissage statistique

Sylvain Arlot 1, 2 
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
Abstract : This text is a tutorial on supervised statistical learning, from the mathematical point of view. We describe the general prediction problem and the two key examples of regression and binary classification. Then, we study two kinds of learning rules: empirical risk minimizers, which naturally lead to convex risks in classification, and local averaging rules, for which a universal consistency result can be obtained. Finally, we identify the limits of learning in order to underline its challenges. The text ends with some useful probabilistic tools and some exercises.
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Submitted on : Wednesday, March 8, 2017 - 10:22:41 PM
Last modification on : Saturday, June 25, 2022 - 10:23:54 PM
Long-term archiving on: : Friday, June 9, 2017 - 2:30:34 PM


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  • HAL Id : hal-01485506, version 1


Sylvain Arlot. Fondamentaux de l'apprentissage statistique. Myriam Maumy-Bertrand; Gilbert Saporta; Christine Thomas-Agnan. Apprentissage statistique et donn\'ees massives, Editions Technip, 2018, 9782710811824. ⟨hal-01485506⟩



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