Evolutionary strategies for hyperparameters of support vector machines based on multi-scale radial basis function kernels |
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Authors: | Tanasanee Phienthrakul Boonserm Kijsirikul |
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Affiliation: | (1) Department of Computer Engineering, Chulalongkorn University, 254 Phyathai, Pathumwan, Bangkok, 10330, Thailand |
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Abstract: | Kernel functions are used in support vector machines (SVM) to compute inner product in a higher dimensional feature space.
SVM classification performance depends on the chosen kernel. The radial basis function (RBF) kernel is a distance-based kernel
that has been successfully applied in many tasks. This paper focuses on improving the accuracy of SVM by proposing a non-linear
combination of multiple RBF kernels to obtain more flexible kernel functions. Multi-scale RBF kernels are weighted and combined.
The proposed kernel allows better discrimination in the feature space. This new kernel is proved to be a Mercer’s kernel.
Furthermore, evolutionary strategies (ESs) are used for adjusting the hyperparameters of SVM. Training accuracy, the bound
of generalization error, and subset cross-validation on training accuracy are considered to be objective functions in the
evolutionary process. The experimental results show that the accuracy of multi-scale RBF kernels is better than that of a
single RBF kernel. Moreover, the subset cross-validation on training accuracy is more suitable and it yields the good results
on benchmark datasets. |
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