Skip to Main content Skip to Navigation
Book sections

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.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-01485506
Contributor : Sylvain Arlot Connect in order to contact the contributor
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

Files

hal_JES_fondamentaux.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01485506, version 1

Citation

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⟩

Share

Metrics

Record views

2291

Files downloads

8160