A Support Vector Machine Based on Maximal Class-Center Margin

The separating hyperplane of traditional support vector machines is sensitive to noises and outliers.When traditional support vector machines separate data containing noises,the obtained hyperplane is not an optimal one.For this problem,a separating hyperplane is designed with the principle of maximizing the distance between two class centers,and a novel support vector machine,called maximal class-center margin support vector machine(MCCM-SVM) is designed.Theoretical analysis and experimental results show that the presented method is correct and effective.