An inexact penalty method for the semiparametric Support Vector Machine classifier

The support vector machine (SVM) classifier has been a popular classification tool used for a variety of pattern recognition tasks. In this study, we compare the performance of a semiparametric SVM classifier derived using an inexact penalty method on the original SVM formulation. This semiparametric form can be easily solved using a sequential decomposition method. We compare the accuracy of the semiparametric SVM against the standard SVM classifier trained using the SMO algorithm. The results indicate that in some cases the semiparametric SVM can give better generalization results than a standard SVM. We also demonstrate several cases where our iterative algorithm solves the SVM problem faster than the SMO.