A Comparative Study of Three Smooth SVM Classifiers

Researching smooth support vector machine (SVM) for classification is an active field in data mining. This paper presents a comparison among three smooth SVM classifiers, smooth SVM, 1st-order polynomial smooth SVM and 2nd-order polynomial smooth SVM. Numerical experiments are performed to compare accuracy and computational complexity of these classifiers with linear and nonlinear kernels. This study provides some guidelines for future research and choosing an appropriate smooth SVM for classification