Double distribution support vector machine

This paper studies the role of the sample mean in binary classifier based on the margin theory. Support Vector Machine (SVM) with maximized minimum margin is widely used in pattern recognition, but it sometimes induces the weak margin distribution which is negative for the generalization performance. Therefore Double Distribution Support Vector Machine (DDSVM) is proposed to obtain strong generalization performance by maximizing the margin distribution of two classes sample means and the minimum margin. The sample mean is usually a good description of samples, and DDSVM can increase the margin distribution and improve the generalization performance. DDSVM is a general learning approach, and its superiority is verified both theoretically and experimentally.