Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with Rbfn Models

Author

Amaury Lendasse, Missouri University of Science and TechnologyFollow
Vincent Wertz
Michel Verleysen

Abstract

This Paper Compares Several Model Selection Methods, based on Experimental Estimates of their Generalization Errors. Experiments in the Context of Nonlinear Time Series Prediction by Radial-Basis Function Networks Show the Superiority of the Bootstrap Methodology over Classical Cross-Validations and Leave-One-Out. © Springer-Verlag Berlin Heidelberg 2003.

Recommended Citation

A. Lendasse et al., "Model Selection with Cross-Validations and Bootstraps - Application to Time Series Prediction with Rbfn Models," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2714, pp. 573 - 580, Springer, Jan 2003.

The definitive version is available at https://doi.org/10.1007/3-540-44989-2_68

Department(s)

Engineering Management and Systems Engineering

International Standard Book Number (ISBN)

978-354040408-8

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