TY - GEN
T1 - SVR kernel parameters selection based on steady-state genetic algorithm
AU - Li, Jie
AU - Gao, Feng
AU - Guan, Xiaohong
AU - Xu, Hui
PY - 2006
Y1 - 2006
N2 - The hyper parameters selection has a great affection on the accuracy of support vector regression algorithm. We chose the optimal hyper parameters including kernel parameters based on steady genetic algorithm for the support vector regression model. Selection of usually used RBF kernel parameters was thoroughly investigated. Two selection strategies for single and diagonal kernel parameters selection were applied on the standard sample data for Boston housing forecasting, and for electrical power demand forecasting. The testing results show that applying steady GA is effective in selecting multiple parameters.
AB - The hyper parameters selection has a great affection on the accuracy of support vector regression algorithm. We chose the optimal hyper parameters including kernel parameters based on steady genetic algorithm for the support vector regression model. Selection of usually used RBF kernel parameters was thoroughly investigated. Two selection strategies for single and diagonal kernel parameters selection were applied on the standard sample data for Boston housing forecasting, and for electrical power demand forecasting. The testing results show that applying steady GA is effective in selecting multiple parameters.
UR - https://www.scopus.com/pages/publications/34047218605
UR - https://www.scopus.com/pages/publications/34047218605#tab=citedBy
U2 - 10.1109/WCICA.2006.1713210
DO - 10.1109/WCICA.2006.1713210
M3 - Conference contribution
AN - SCOPUS:34047218605
SN - 1424403324
SN - 9781424403325
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 4405
EP - 4409
BT - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
T2 - 6th World Congress on Intelligent Control and Automation, WCICA 2006
Y2 - 21 June 2006 through 23 June 2006
ER -