An information-geometrical approach to constructing kernel in support vector regression machines

Authors:
Wensen An;Yanguang Sun
Affiliations:
Department of Automation, University of Science and Technology of China, Hefei, China;Automation Research and Design Institute of Metallurgical Industry, Beijing, China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

The type of kernel function has a great important influence on the performance of support vector machines (SVMs); however, there is no theoretical guidance to choose a good kernel. To solve classification problem, Amari presented a method of modifying kernel based on information geometry theory. In the paper, we first review the classical formulation of regression problem, then propose an approach to constructing the kernel function in support vector regression machines from information-geometrical viewpoint, and point out its difference with the method that Amari used in support vector classification machines. Finally some simulation results show the effectiveness of the proposed method.