Study on Classification Method Based on Support Vector Machine

被引:5
作者
Men, Hong [1 ]
Gao, Yanchun [1 ]
Wu, Yujie [1 ]
Li, Xiaoying [1 ]
机构
[1] NE Dianli Univ, Sch Automat Engn, Jilin, Peoples R China
来源
PROCEEDINGS OF THE FIRST INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND COMPUTER SCIENCE, VOL II | 2009年
关键词
classification; support vector machine; neural network; kernel function;
D O I
10.1109/ETCS.2009.344
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Classification experiments are made with neural network algorithm and support vector machine method separately. The samples are divided into three groups and two kinds of support vector machines based on polynomial kernel and radial basis function are applied by changing the parameter values. The simulated results show that, as for the dataset with less training samples, using simple structure learning function will avoid the over fitting problem. In contrast, the learning function with slightly simple structure will reduce the generalization ability. In the experiment, the Penalty factor C is introduced in order to allow the training samples to be classified wrongly. Increasing the value of C, generalization ability of the learning machine can be improved. Using cross-validation method to choose parameter values can improve the classification accuracy. The experimental results show that the support vector machine method is superior to the neural network algorithm.
引用
收藏
页码:369 / 373
页数:5
相关论文
共 7 条
[1]  
Cheng Yurong, 2003, Journal of University of Electronic Science and Technology of China, V32, P469
[2]  
[代六玲 Dai Liuling], 2004, [中文信息学报, Journal of Chinese Information Processing], V18, P26
[3]  
LIU JH, 2003, COMPUTER ENG APPL, V23, P81
[4]  
SUN DS, 2004, COMPUTER ENG APPL, V20, P54
[5]  
WANG JQ, 2004, J IMAGE GRAPHICS, V9, P1075
[6]  
Yang Yiming, 1997, P 14 INT C MACH LEAR, P412
[7]  
ZHANG XG, 2000, AUTOMATION T, V9, P32