PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM
Yoan Miche, Patrick Bas, Christian Jutten, Olli Simula and Amaury Lendasse
In: ESANN 2008, European Symposium on Artificial Neural Networks, April 23-25 2008, Bruges, Belgium.

Abstract

This paper proposes a methodology named OP-ELM, based on a recent development –the Extreme Learning Machine– decreasing drastically the training speed of networks. Variable selection is beforehand performed on the original dataset for proper results by OP-ELM: the net- work is first created using Extreme Learning Process, selection of the most relevant nodes is performed using Least Angle Regression (LARS) ranking of the nodes and a Leave-One-Out estimation of the performances. Results are globally equivalent to LSSVM ones with reduced computational time.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4800
Deposited By:Amaury Lendasse
Deposited On:24 March 2009