PAC-Bayesian bounds for randomized empirical risk minimizers |
| |
Authors: | P Alquier |
| |
Institution: | (1) CREST and Laboratoire de Probab. et Modèles Aléatoires (Univ. Paris 7), Paris, France |
| |
Abstract: | The aim of this paper is to generalize the PAC-Bayesian theorems proved by Catoni 6, 8] in the classification setting to
more general problems of statistical inference. We show how to control the deviations of the risk of randomized estimators.
A particular attention is paid to randomized estimators drawn in a small neighborhood of classical estimators, whose study
leads to control of the risk of the latter. These results allow us to bound the risk of very general estimation procedures,
as well as to perform model selection.
|
| |
Keywords: | regression estimation classification adaptive inference statistical learning randomized estimator empirical risk minimization empirical bound |
本文献已被 SpringerLink 等数据库收录! |