Basically, the support vector machine is a binary learning machine with some highly elegant properties. Given a training sample, the support vector machine constructs a hyperplane as the decision surface in such a way that the margin of separation between positive and negative examples is maximized. Maximizing the margin of separation between binary classes is equivalent to minimizing the Euclidean norm of the weight vector w.