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. © Springer-Verlag Berlin Heidelberg 2003.
CITATION STYLE
Lendasse, A., Wertz, V., & Verleysen, M. (2003). Model selection with cross-validations and bootstraps - Application to time series prediction with RBFN models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, 573–580. https://doi.org/10.1007/3-540-44989-2_68
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