Parameter selection for Gaussian radial basis function in support vector machine classification

The Gaussian radial basis function is widely used in the support vector machine (SVM) due to its attractive characteristics. The parameter (σ) in this kernel is crucial to robust performance of SVM. In this paper, we derive a formula to compute the optimal s under the principle of maximizing the class separability in the kernel space. The most attractive feature of the proposed method is that no optimization search algorithm is required in parameter selection; and thus our method is computational effective. The experimental results demonstrate the proposed method is fast and robust.