Kernel parameter selection method based on estimation of convex

The selection of the kernel and the corresponding parameter is one of the key problems for support vector machine(SVM).On the basis of the statistical learning theory(SLT),a new way to select the optimal kernel parameter by the estimation of approximate convex of the sample set is presented,in which the division of the sample set by the degree is adopted.The presented method overcome some disadvantages such as high computation cost,which exist in the traditional optimization-based methods.Moreover,it is used no matter whether the data set is dense or whether the distribution is uniform.The simulation experiments demonstrate the feasibility and the effectiveness of the presented method.