A Grid-based ACO Algorithm for Parameters Optimization in Support Vector Machines

被引：3

作者：

Zhang, XiaoLi ^{[1
]}

Chen, XueFeng ^{[1
]}

Zhang, ZhouSuo ^{[1
]}

He, ZhengJia ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Mech Engn, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China

来源：

2008 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, VOLS 1 AND 2 | 2008年

关键词：

D O I：

10.1109/GRC.2008.4664645

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The parameters optimization of the penalty constant C and the bandwidth of the radial basis function (RBF) kernel or is an important step in establishing an efficient and high-performance support vector machines (SVMs) model. Aiming at optimizing the parameters of SVMs, this paper presents a grid-based ant colony optimization (ACO) algorithm to choose parameters C and a automatically for SVMS instead of selecting parameters randomly by human's experience, so that the generalization error can be reduced and the generalization performance can be improved simultaneously. Some experimental results confirm the feasibility and efficiency of the approach.

引用

页码：805 / 808

页数：4

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