Smoothed discrepancy principle as an early ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Smoothed discrepancy principle as an early stopping rule in RKHS
Auteur(s) :
Averyanov, Yaroslav [Auteur]
Inria Lille - Nord Europe
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Inria Lille - Nord Europe
Inria Lille - Nord Europe
Celisse, Alain [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
Inria Lille - Nord Europe
Titre de la manifestation scientifique :
51es Journées de Statistique
Ville :
Nancy
Pays :
France
Date de début de la manifestation scientifique :
2019-06
Mot(s)-clé(s) en anglais :
Non-parametric regression
Regularization
Kernels
Stopping rules
regularization
kernels
stopping rules
Regularization
Kernels
Stopping rules
regularization
kernels
stopping rules
Discipline(s) HAL :
Statistiques [stat]
Statistiques [stat]/Machine Learning [stat.ML]
Statistiques [stat]/Machine Learning [stat.ML]
Résumé en anglais : [en]
In this paper we work on the estimation of a regression function that belongs to a polynomial decay reproducing kernel Hilbert space (RKHS). We describe spectral filter framework for our estimator that allows us to deal ...
Lire la suite >In this paper we work on the estimation of a regression function that belongs to a polynomial decay reproducing kernel Hilbert space (RKHS). We describe spectral filter framework for our estimator that allows us to deal with several iterative algorithms: gradient descent, Tikhonov regularization, etc. The main goal of the paper is to propose a new early stopping rule by introducing smoothing parameter for empirical risk of the estimator in order to improve the previous results [1] on discrepancy principle. Theoretical justifications as well as simulations experiments for the proposed rule are provided.Lire moins >
Lire la suite >In this paper we work on the estimation of a regression function that belongs to a polynomial decay reproducing kernel Hilbert space (RKHS). We describe spectral filter framework for our estimator that allows us to deal with several iterative algorithms: gradient descent, Tikhonov regularization, etc. The main goal of the paper is to propose a new early stopping rule by introducing smoothing parameter for empirical risk of the estimator in order to improve the previous results [1] on discrepancy principle. Theoretical justifications as well as simulations experiments for the proposed rule are provided.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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