An Accelerated Coordinate Descent Method for Support Vector Machine

Support vector machines (SVMs) are famous and important classifiers in classification researches. SVM and its application have been used in an enormous amount of research in various scientific domains in recent years. There are different methods to solve SVM problems. Coordinate descent methods are one of the main categories of methods for solving these problems. In this paper, using the generalization of Aitken’s $\Delta 2$ process to the vector case, a new accelerated coordinate descent algorithm is proposed. The new algorithm is tested with three different datasets. The numerical results indicate the efficiency of the new algorithm in the concept of increasing the speed of convergence.