One big difficulty in the practical use of support vector machines is the selection of a suitable kernel function and its appropriate parameter setting for a given application. There is no rule for the selection and people have to estimate the machine’s performance based on a costly multi-trial iteration of training and testing phases. In this paper, we describe a method to reduce the model sel...
