Bootstrap for Model Selection: Linear Approximation of the Optimism

Author

G. Simon
Amaury Lendasse, Missouri University of Science and TechnologyFollow
M. Verleysen

Abstract

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. © Springer-Verlag Berlin Heidelberg 2003.

Recommended Citation

G. Simon et al., "Bootstrap for Model Selection: Linear Approximation of the Optimism," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2686, pp. 182 - 189, Springer, Jan 2003.

The definitive version is available at https://doi.org/10.1007/3-540-44868-3_24

Department(s)

Engineering Management and Systems Engineering

International Standard Serial Number (ISSN)

1611-3349; 0302-9743

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Springer, All rights reserved.

Publication Date

01 Jan 2003