Model selection with cross-validations and bootstraps: application to time series prediction with RBFN models

Authors:
Amaury Lendasse;Vincent Wertz;Michel Verleysen
Affiliations:
Université catholique de Louvain, CESAME, Louvain-la-Neuve, Belgium;Université catholique de Louvain, CESAME, Louvain-la-Neuve, Belgium;Université catholique de Louvain, DICE, Louvain-la-Neuve, Belgium
Venue:
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Year:
2003

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Abstract

This paper compares several model selection methods, based on experimental estimates of their generalization errors. Experiments in the context of nonlinear time series prediction by Radial-Basis Function Networks show the superiority of the bootstrap methodology over classical cross-validations and leave-one-out.