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

摘要

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.