T. Phienthrakul and B. Kijsirikul (Thailand)
Computational Intelligence, Evolutionary Strategies, Support Vector Machines, Radial Basis Function
In support vector machines (SVMs), kernel functions are used to compute dot product in a higher dimensional feature space. The performance of classification depends on the chosen kernel. The radial basis function (RBF) kernel is a most popular kernel that succeeded in many tasks. In order to obtain a more flexible kernel function, a family of RBF kernels is proposed. Multi-scale RBF kernels are combined by including weights. This new kernel is proved to be a Mercer's kernel. Then, the evolutionary strategies (ESs) are used to adjust the weights and the widths of the RBF kernels. The training accuracy, the bound of generalization error, and subsets cross-validation are used to be objective functions in the evolutionary process. The experimental results show that the proposed kernel allows better discrimination in the feature space. Moreover, subsets cross-validation is a good objective function and yields the effective results on benchmarks.
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