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ISNN
2005
Springer

Scaling the Kernel Function to Improve Performance of the Support Vector Machine

9 years 9 months ago
Scaling the Kernel Function to Improve Performance of the Support Vector Machine
Abstract. The present study investigates a geometrical method for optimizing the kernel function of a support vector machine. The method is an improvement of the one proposed in [4, 5]. It consists of using prior knowledge obtained from conventional SVM training to conformally rescale the initial kernel function, so that the separation between two classes of data is effectively enlarged. It turns out that the new algorithm works efficiently, has few free parameters, consumes very low computational cost, and overcomes the susceptibility of the original method.
Peter Williams, Sheng Li, Jianfeng Feng, Si Wu
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ISNN
Authors Peter Williams, Sheng Li, Jianfeng Feng, Si Wu
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