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