Fast bootstrap for Least-Square Support Vector Machines

Lendasse, Amaury;Simon, Geoffroy;Wertz, Vincent;Verleysen, Michel
(2004) ESANN 2004, European Symposium on Artificial Neural Networks — Location: Bruges (Belgium) (28.April.2004)

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Authors
  • Lendasse, AmauryHelsinky University of Technology
    Author
  • Simon, GeoffroyUCLouvain
    Author
  • Wertz, VincentUCLouvain
    Author
  • Verleysen, MichelUCLouvain
    Author
Abstract
The Bootstrap resampling method may be efficiently used to estimate the generalization error of nonlinear regression models, as artificial neural networks and especially Least-square Support Vector Machines. Nevertheless, the use of the Bootstrap implies a high computational load. In this paper we present a simple procedure to obtain a fast approximation of this generalization error with a reduced computation time. This proposal is based on empirical evidence and included in a simulation procedure.
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Lendasse, A., Simon, G., Wertz, V., & Verleysen, M. (2004). Fast bootstrap for Least-Square Support Vector Machines. Proceedings of ESANN 2004, European Symposium on Artificial Neural Networks, p. 525-530. https://hdl.handle.net/2078.5/221765