Fast approximation of the bootstrap for model selection

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.

• Type: Conference
• Year: 2003
• Where: ESANN
• Authors: Geoffroy Simon, Amaury Lendasse, Vincent Wertz, Michel Verleysen
• Keywords: Bootstrap Resampling Method, ESANN 2003, ESANN 2007, Generalization Error, Nonlinear Regression Models