Abstract
In This Paper, We Present a New Method to Perform Model Structure Selection. This Proposed Method Can Be Used to Select the Complexity of Any Continuous Regression Method. We Also Present an Asymptotic Mathematical Proof of the Proposed Method and the New Method is Illustrated on a Benchmark. Compared to the Well-Known 10-Fold Cross-Validation, the Computational Time Associated to Our New Method is Approximately Divided by a Factor 8 as Illustrated on the Benchmark.
Recommended Citation
A. Lendasse et al., "NNBMSS: A Novel and Fast Method for Model Structure Selection," ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 381 - 386, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Jan 2021.
The definitive version is available at https://doi.org/10.14428/esann/2021.ES2021-9
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-287587082-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, All rights reserved.
Publication Date
01 Jan 2021
