PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

OP-ELM: Theory, Experiments and a Toolbox
Yoan Miche, Antti Sorjamaa and Amaury Lendasse
In: LNCS - Artificial Neural Networks - ICANN 2008 - Part I Lecture Notes in Computer Science , 5163/2008 . (2008) Springer Berlin / Heidelberg , Prague, Czech Republic , pp. 145-154.

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.

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EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4784
Deposited By:Amaury Lendasse
Deposited On:24 March 2009