An efficient method for tuning kernel parameter of the support vector machine

We propose a new method for searching the kernel parameter of the support vector machine on the basis of the distribution of data in the feature space. Although the distribution (structure) of data is unknown in the feature space, it depends on the kernel parameter. The distribution of data is characterized by the principal component analysis method. Thus, simple eigenanalysis method is applied to the matrix of the same dimension as the kernel matrix to find the kernel parameter. Therefore, this method is very fast. The proposed method can obtain the kernel parameter graphically.