Conformal Transformation of Kernel Functions: A Data-Dependent Way to Improve Support Vector Machine Classifiers

Authors:
Si Wu;Shun-Ichi Amari
Affiliations:
RIKEN Brain Science Institute, Hirosawa 2-1, Wako-shi, Saitama, Japan. (Present address: Department of Computer Science, Sheffield University, UK);RIKEN Brain Science Institute, Hirosawa 2-1, Wako-shi, Saitama, Japan
Venue:
Neural Processing Letters
Year:
2002

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Abstract

In this paper we extend the conformal method of modifying a kernel function to improve the performance of Support Vector Machine classifiers [14, 15]. The kernel function is conformally transformed in a data-dependent way by using the information of Support Vectors obtained in primary training. We further investigate the performances of modified Gaussian Radial Basis Function and Polynomial kernels. Simulation results for two artificial data sets show that the method is very effective, especially for correcting bad kernels.