@inproceedings{601ea80e024842e899d95c8aef3a12ce,
title = "SVR kernel parameters selection based on steady-state genetic algorithm",
abstract = "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.",
keywords = "Hyper parameters selection, Kernel parameters, Steady-state genetic algorithm, Support vector machine",
author = "Jie Li and Feng Gao and Xiaohong Guan and Hui Xu",
year = "2006",
doi = "10.1109/WCICA.2006.1713210",
language = "英语",
isbn = "1424403324",
series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
pages = "4405--4409",
booktitle = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",
note = "6th World Congress on Intelligent Control and Automation, WCICA 2006 ; Conference date: 21-06-2006 Through 23-06-2006",
}