Model Selection for 2-norm Support Vector Machine Based on Improved RM Bound

Calculating the radius of radius-margin (RM) bound by solving the quadratic programming adds the computational overload. In order to solve this problem, we construct a new RM bound which approximates the radius by using the maximum pairwise distance over all points. Then based on new RM bound, the model selection of 2-norm SVM (L2-SVM) was conducted, and automatically adjusted parameters by employing the gradient descent algorithm. Finally, the classification accuracy and computational efficiency of the algorithm were discussed through simulation experiments.The experimental results show that the classification accuracy of the proposed algorithm is not significantly changed compared with the model selection based on RM bound, but the computational efficiency is improved at least one fold.