Bootstrap for Model Selection: Linear Approximation of the Optimism
نویسندگان
چکیده
The bootstrap resampling method may be efficiently used to estimate the generalization error of nonlinear regression models, as artificial neural networks. 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 suggested simulation procedure.
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