where w is a normal vector perpendicular to the hyperplane and ξ i are slake variables for allowing misclassifications. Here C (>0) is the penalty parameter which balances the trade-off between the margin and the training error. In this study, LIBSVM package [46, 47] with radial basis kernel function is used. Two parameters, the regularization parameter C and the kernel width parameter γ are optimized based on 5-fold cross-validation using a grid search strategy.