Call for NIPS 2008 Kernel Learning Workshop Submissions
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S. Amari and S. Wu (1999)
Improving support vector machines by modifying kernel functions
Neural Networks:783-789.
We propose a method of modifying a kernel function to improve the performance of a support vector machine classifier. This is based on the structure of the Riemannian geometry induced by the kernel function. The idea is to enlarge the spatial resolution around the separating boundary surface, by a conformal transformation, such that the separability between classes is increased.