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2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery
Parameter Selection of Support Vector Regression Machine Based on Differential Evolution Algorithm
Tianjin, China
August 14-August 16
ISBN: 978-0-7695-3735-1
ASCII Text
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Qing Yu, Ying Liu, Feng Rao, "Parameter Selection of Support Vector Regression Machine Based on Differential Evolution Algorithm," Fuzzy Systems and Knowledge Discovery, Fourth International Conference on, vol. 2, pp. 596-598, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009.
 
 
BibTex
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@article{ 10.1109/FSKD.2009.846,
author = {Qing Yu and Ying Liu and Feng Rao},
title = {Parameter Selection of Support Vector Regression Machine Based on Differential Evolution Algorithm},
journal ={Fuzzy Systems and Knowledge Discovery, Fourth International Conference on},
volume = {2},
year = {2009},
isbn = {978-0-7695-3735-1},
pages = {596-598},
doi = {http://doi.ieeecomputersociety.org/10.1109/FSKD.2009.846},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on
TI - Parameter Selection of Support Vector Regression Machine Based on Differential Evolution Algorithm
SN - 978-0-7695-3735-1
SP596
EP598
A1 - Qing Yu,
A1 - Ying Liu,
A1 - Feng Rao,
PY - 2009
KW - e-Support Vector Regression Machine( e-SVRM)
KW - Differential Evolution(DE)
KW - parameter optimization
VL - 2
JA - Fuzzy Systems and Knowledge Discovery, Fourth International Conference on
ER -
 
This parameters selection is an important issue in the research of ε-support vector regression machine ( ε-SVRM), whose nature is an optimization selection process. Motivated by the effectiveness of Differential Evolution (DE) algorithm on optimization problem, a new automatic searching method based on DE algorithm was proposed. Experimental results demonstrate that ε-SVRM model optimization based on DE algorithm has better prediction capability compared with the methods based on Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO).
Index Terms:
e-Support Vector Regression Machine( e-SVRM), Differential Evolution(DE), parameter optimization
Citation:
Qing Yu, Ying Liu, Feng Rao, "Parameter Selection of Support Vector Regression Machine Based on Differential Evolution Algorithm," fskd, vol. 2, pp.596-598, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009
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