An ICDF-Based Fast Parameter Optimization Approach for Support Vector Machines

被引：0
|
作者：
Wang J.-P. [1 ]
Hu Y.-M. [1 ]
Luo J.-X. [1 ]
机构：
[1] School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, Guangdong
来源：
Luo, Jia-Xiang (luojx@scut.edu.cn) | 1600年 / South China University of Technology卷 / 45期
基金：
中国国家自然科学基金;
关键词：
Inter-cluster distance; Kernel parameter; Modified golden section algorithm; Parameter optimization; Support vector machine;
D O I：
10.3969/j.issn.1000-565X.2017.07.019
中图分类号：
学科分类号：
摘要：
In the process of parameter optimization for support vector machines (SVMs) with Gaussian kernel, inter-cluster distance in feature spaces (ICDF) is an effective measure. However, ICDF may result in heavy computational load and large time consumption. In order to solve this problem, firstly, the theorem that ICDF is a positive strictly-unimodal function about Gaussian kernel parameter is proved. Then, according to this theorem, a modified golden section algorithm (MGSA) is proposed to search a shrunk value fast for kernel parameter in the candidate set. Thus, a fast parameter optimization approach on the basis of both MGSA and differential evolutionary algorithm is presented. Finally, some experiments are carried out to verify the effectiveness and rapidity of the proposed approach. © 2017, Editorial Department, Journal of South China University of Technology. All right reserved.
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页码：135 / 142
页数：7
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