Parameter Optimization for SVM using Sequential Number Theoretic for Optimization

In this paper, we propose a support vector machine (SVM) meta-parameter optimization method which uses sequential number theoretic optimization (SNTO) and gradient information for better optimization performance. SNTO is a new global optimization approach whose foundation is numeric and statistic theory. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. Simulations demonstrate that it is robust and works effectively and efficiently on a variety of problems