Optimal Gaussian Kernel Parameter Selection for SVM Classifier
The performance of the kernel based learning algorithms such as SVM depends heavily on the proper choice of the kernel parameter It is desirable for the kernel machines to work on the optimal kernel parameter that adapts well to the input data and the learning tasks In this paper we present a novel method for selecting Gaussian kernel parameter by maximizing a class separability criterion which measures the data distribution in the kernel induced feature space and is invariant under any non singular linear transformation The experimental results show that both the class separability of the data in the kernel induced feature space and the classification performance of the SVM classifier are improved by using the optimal kernel parameter
kernel parameter selection kernel optimization model selection support vector machines pattern recognition