Fast bootstrap for least-square support vector machines
نویسندگان
چکیده
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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Fast bootstrap methodology for regression model selection
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