Journal of Computer Science and Technology

, Volume 17, Issue 5, pp 549–555

Relationship between support vector set and kernel functions in SVM

Article

DOI: 10.1007/BF02948823

Cite this article as:
Zhang, L. & Zhang, B. J. Comput. Sci. & Technol. (2002) 17: 549. doi:10.1007/BF02948823

Abstract

Based on a constructive learning approach, covering algorithms, we investigate the relationship between support vector sets and kernel functions in support vector machines (SVM). An interesting result is obtained. That is, in the linearly non-separable case, any sample of a given sample setK can become a support vector under a certain kernel function. The result shows that when the sample setK is linearly non-separable, although the chosen kernel function satisfies Mercer’s condition its corresponding support vector set is not necessarily the subset ofK that plays a crucial role in classifyingK. For a given sample set, what is the subset that plays the crucial role in classification? In order to explore the problem, a new concept, boundary or boundary points, is defined and its properties are discussed. Given a sample setK, we show that the decision functions for classifying the boundary points ofK are the same as that for classifying theK itself. And the boundary points ofK only depend onK and the structure of the space at whichK is located and independent of the chosen approach for finding the boundary. Therefore, the boundary point set may become the subset ofK that plays a crucial role in classification. These results are of importance to understand the principle of the support vector machine (SVM) and to develop new learning algorithms.

Keywords

support vector machine (SVM) support vector kernel function constructive learning theory cover boundary 

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2002

Authors and Affiliations

  1. 1.Artificial Intelligence InstituteAnhui UniversityAnhuiP.R. China
  2. 2.Department of Computer ScienceTsinghua UniversityBeijingP.R. China
  3. 3.State Key Lab of Intelligent Technology and SystemTsinghua UniversityBeijingP.R. China

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