The bootstrap resampling method may be efficiently used to estimate the generalization error of a family of nonlinear regression models, as artificial neural networks. The main difficulty associated with the bootstrap in real-world applications is the high computation load. In this paper we propose a simple procedure based on empirical evidence, to considerably reduce the computation time needed to estimate the generalization error of a family of models of increasing number of parameters.
['Geoffroy Simon', 'Amaury Lendasse', 'Vincent Wertz', 'Michel Verleysen']
Fast approximation of the bootstrap for model selection