Research on Kernel Function of Support Vector Machine

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 260)

Abstract

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 

Notes

Acknowledgments

This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, and Beijing Natural Science Foundation under Grant 4112045, and the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024.

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  2. 2.Key Laboratory of Communication and Information SystemsBeijing Municipal Commission of Education, Beijing Jiaotong UniversityBeijingChina
  3. 3.China Information Technology Security Evaluation CenterBejingChina

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