Op-Elm: Theory, Experiments and a Toolbox
Abstract
This Paper Presents the Optimally-Pruned Extreme Learning Machine (Op-Elm) Toolbox. This Novel, Fast and Accurate Methodology is Applied to Several Regression and Classification Problems. the Results Are Compared with Widely Known Multilayer Perceptron (Mlp) and Least-Squares Support Vector Machine (Ls-Svm) Methods. as the Experiments (Regression and Classification) Demonstrate, the Op-Elm Methodology is Considerably Faster Than the Mlp and the Ls-Svm, While Maintaining the Accuracy in the Same Level. Finally, a Toolbox Performing the Op-Elm is Introduced and Instructions Are Presented. © Springer-Verlag Berlin Heidelberg 2008.
Recommended Citation
Y. Miche et al., "Op-Elm: Theory, Experiments and a Toolbox," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5163 LNCS, no. PART 1, pp. 145 - 154, Springer, Dec 2008.
The definitive version is available at https://doi.org/10.1007/978-3-540-87536-9_16
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-354087535-2
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
01 Dec 2008
