Fast approximation of the bootstrap for model selection
(2003) ESANN 2003, European Symposium on Artificial Neural Networks — Location: Bruges (Belgium) (23.April.2003)
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- Abstract
- 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.
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Citations
Simon, G., Lendasse, A., Wertz, V., & Verleysen, M. (2003). Fast approximation of the bootstrap for model selection. Proceedings of ESANN 2003, European Symposium on Artificial Neural Networks, p. 475-480. https://hdl.handle.net/2078.5/226016