Extreme Learning Machine: A Robust Modeling Technique? Yes!
Abstract
In This Paper is Described the Original (Basic) Extreme Learning Machine (Elm). Properties Like Robustness and Sensitivity to Variable Selection Are Studied. Several Extensions of the Original Elm Are Then Presented and Compared. Firstly, Tikhonov-Regularized Optimally-Pruned Extreme Learning Machine (Trop-Elm) is Summarized as an Improvement of the Optimally-Pruned Extreme Learning Machine (Op-Elm) in the Form of a L 2 Regularization Penalty Applied within the Op-Elm. Secondly, a Methodology to Linearly Ensemble Elm (-Elm) is Presented in Order to Improve the Performance of the Original Elm. These Methodologies (Trop-Elm and -Elm) Are Tested Against State of the Art Methods Such as Support Vector Machines or Gaussian Processes and the Original Elm and Op-Elm, on Ten Different Data Sets. a Specific Experiment to Test the Sensitivity of These Methodologies to Variable Selection is Also Presented. © 2013 Springer-Verlag Berlin Heidelberg.
Recommended Citation
A. Lendasse et al., "Extreme Learning Machine: A Robust Modeling Technique? Yes!," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7902 LNCS, no. PART 1, pp. 17 - 35, Springer, Jul 2013.
The definitive version is available at https://doi.org/10.1007/978-3-642-38679-4_2
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-364238678-7
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
17 Jul 2013
