SVM parameter selection algorithm based on maximum kernel similarity diversity

Aiming at support vector machine (SVM) parameter selection problem, a novel Gaussian kernel parameter rapid selection algorithm is proposed on the basis of kernel similarity diversity maximum (MSD) by analyzing the equivalent network model and the classification principle of SVM. In addition, MSD is combined with parameter search algorithm based on cross validation, and thus it is a composite parameter selection algorithm (MSD-GS) to the realize rapid and optimal selection of kernel parameter and regularization parameter. Simulation experiment results on data sets from UCI show that MSD-GS has the merits of simpleness, celerity and accurate parameter selection with no need of adding prior knowledge. The parameter selection result is better than the traversing exponential grid search algorithm. The selected couple of SVM parameters can make SVM get high generalization performance.