A Methodology for Building Regression Models using Extreme Learning Machine: Op-Elm
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.
Recommended Citation
Y. Miche et al., "A Methodology for Building Regression Models using Extreme Learning Machine: Op-Elm," ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, pp. 247 - 252, European Symposium on Artificial Neural Networks, Dec 2008.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030708-4
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2008
