Fast bootstrap for Least-Square Support Vector Machines

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

Fast bootstrap for least-square support vector machines

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. 1