Estimating Parameters of Kernel Functions in Support Vector Learning
Yi-Chao Chan, Wan-Jui Lee, Shie-Jue Lee · 2006
The selection and modification of kernel functions is a very important but rarely studied problem in the field of support vector learning. However, the kernel function of a support vector machine has great influence on its performance. The kernel function projects the dataset from the original data space into the feature space, and therefore the problems which can't be done in low dimensions could be done in a higher dimension through the transform of the kernel function. In this paper, we adopt the FCM clustering algorithm to group data patterns into clusters, and then use a statistical approach to calculate the standard deviation of each pattern with respect to the other patterns in the same cluster. Therefore we can make a proper estimation on the distribution of kernel functions. Experimental results have shown that our approach can derive better kernel functions than other methods, and also can have better learning and generalization abilities.