A Novel Model for Selecting Parameters of SVM with RBF Kernel

被引:0
作者
Yan, Zhi-gang [1 ]
Ding, Yun-jing [1 ]
机构
[1] China Univ Min & Technol, Key Lab Land Environm & Disaster Monitoring SBSM, Xuzhou 221116, Peoples R China
来源
PROCEEDINGS OF THE 10TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA 2012) | 2012年
关键词
support vector machine; RBF kernel; generalization ability; bilinear-grid search method instruction; SUPPORT VECTOR MACHINES;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Based on the viewpoint of similarity measurement, researched the influences of the error penalty parameter C and the RBF kernel parameter sigma on support vector machine's generalization ability. As the result, the parameter C adjust the similarities between the sample categories and sigma adjust the similarities among the samples, C and mutually restrict and balance each other in a certain range, the shape of the optimal parameter range like a fan, the more reasonable parameters' value locate at the center of the fan, where the values of C and sigma are smaller. A novel method for selecting parameters was presented, firstly, roughly grid searched the reasonable parameter range with a big step size, then selected the optimized parameters in the delineated area through bilinear-grid search method finely. Experiment results show that the improved method has a better performance both at accuracy and speed, moreover, which can avoid excessive values and enhance the stability.
引用
收藏
页码:566 / 569
页数:4
相关论文
共 3 条
  • [1] Choosing multiple parameters for support vector machines
    Chapelle, O
    Vapnik, V
    Bousquet, O
    Mukherjee, S
    [J]. MACHINE LEARNING, 2002, 46 (1-3) : 131 - 159
  • [2] Asymptotic behaviors of support vector machines with Gaussian kernel
    Keerthi, SS
    Lin, CJ
    [J]. NEURAL COMPUTATION, 2003, 15 (07) : 1667 - 1689
  • [3] An overview of statistical learning theory
    Vapnik, VN
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 1999, 10 (05): : 988 - 999