Modifications of kernels to improve support vector machine classifiers

被引：0
|
作者：
Shen, RM [1 ]
Fu, YG [1 ]
Zhang, TZ [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
support vector machine; kernel function; nonlinear classification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Kernel function is a key factor in support vector machine classifiers. In this paper, we put forward a new conformal transformation on kernel functions to improve the performance of support vector machine classifiers, which is based on the method of Amari's idea. We have some important modifications and make the method more robust with respect to the input data distribution and have greater generalization ability with noisy, data. We have also studied the performance of the modified kernels on the Gaussian RBF and Polynomial kernels when a kernel is modified iteratively several times. Simulation results for the data set comparing to the two former methods show remarkable improvement in generalization errors.
引用
收藏
页码：3313 / 3317
页数：5
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