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Research on Kernel Function of Support Vector Machine
Liu Lijuan 1 #,Shen Bo 2 *,Wang Xing 3
1.School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044
2.School of Electronic and Information Engineering, Beijing Jiaotong University, 100044
3.China Information Technology Security Evaluation Center, 100085
*Correspondence author
#Submitted by
Subject:
Funding: National Natural Science Foundation of China under Grant (No.No. 61172072, 61271308), the Beijing Natural Science Foundation under Grant (No.No. 4112045), the Beijing Science and Technology Program under Grant (No.No. Z121100000312024), the Research Fund for the Doctoral Program of Higher Education of China under Grant (No.No. 20100009110002)
Opened online: 8 July 2013
Accepted by: none
Citation: Liu Lijuan,Shen Bo,Wang Xing.Research on Kernel Function of Support Vector Machine[OL]. [ 8 July 2013] http://en.paper.edu.cn/en_releasepaper/content/4549429
 
 
Support Vector Machine is a kind of algorithm used for classifying linear and nonlinear data, which not only has a solid theoretical foundation, but is more accurate than other sorting algorithms in many areas of applications, especially in dealing with high-dimensional data. It is not necessary for us to get the specific mapping function in solving quadratic optimization problem of SVM, and the only thing we need to do is to use kernel function to replace the complicated calculation of the dot product of the data set, reducing the number of dimension calculation. This paper introduces the theoretical basis of support vector machine, summarizes the research status and analyses the research direction and development prospects of kernel function.
Keywords:support vector machine; high-dimension data; kernel function; quadratic optimization
 
 
 

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