  Support vector machine (SVM) has been one of the most popular learning
algorithms, with the central idea of maximizing the minimum margin, i.e., the
smallest distance from the instances to the classification boundary. Recent
theoretical results, however, disclosed that maximizing the minimum margin does
not necessarily lead to better generalization performances, and instead, the
margin distribution has been proven to be more crucial. In this paper, we
propose the Large margin Distribution Machine (LDM), which tries to achieve a
better generalization performance by optimizing the margin distribution. We
characterize the margin distribution by the first- and second-order statistics,
i.e., the margin mean and variance. The LDM is a general learning approach
which can be used in any place where SVM can be applied, and its superiority is
verified both theoretically and empirically in this paper.
