OP-ELM: Theory, Experiments and a Toolbox
Yoan Miche, Antti Sorjamaa and Amaury Lendasse
LNCS - Artificial Neural Networks - ICANN 2008 - Part I
Lecture Notes in Computer Science
Springer Berlin / Heidelberg
, Prague, Czech Republic
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.