Least squares support vector machine with parametric margin for binary classification

In this paper, we propose a least squares support vector machine with parametric margin (Par-LSSVM) for binary classification, which only needs to solve a system of linear equation. Par-LSSVM is able to handle the datasets with heteroscedastic noise. And the closer hyperplane to the test data point gives the class label, and this makes Par-LSSVM capable of dealing with "Cross Planes" datasets. The experimental results on several artificial, benchmark and USPS datasets indicate that our proposed algorithm outperforms Par-ν-SVM for binary classification problem.