  Support vector regression (SVR) is one of the most popular machine learning
algorithms aiming to generate the optimal regression curve through maximizing
the minimal margin of selected training samples, i.e., support vectors. Recent
researchers reveal that maximizing the margin distribution of whole training
dataset rather than the minimal margin of a few support vectors, is prone to
achieve better generalization performance. However, the margin distribution
support vector regression machines suffer difficulties resulted from solving a
non-convex quadratic optimization, compared to the margin distribution strategy
for support vector classification, This paper firstly proposes a maximal margin
distribution model for SVR(MMD-SVR), then implementing coupled constrain factor
to convert the non-convex quadratic optimization to a convex problem with
linear constrains, which enhance the training feasibility and efficiency for
SVR to derived from maximizing the margin distribution. The theoretical and
empirical analysis illustrates the superiority of MMD-SVR. In addition,
numerical experiments show that MMD-SVR could significantly improve the
accuracy of prediction and generate more smooth regression curve with better
generalization compared with the classic SVR.
