Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with RBFN Models

نویسندگان

  • Amaury Lendasse
  • Vincent Wertz
  • Michel Verleysen
چکیده

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.

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تاریخ انتشار 2003