A methodology for Building Regression Models using Extreme Learning Machine: OP-ELM
Yoan Miché, Patrick Bas, Christian Jutten, Olli Simula, Amaury Lendasse · 2008
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 network 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. 1