Figure 2: 2D space illustration of the decision boundary of the support vector machine (SVM) linear classifier. (a) the hard margin on linearly separable examples where no training errors are permitted. (b) the soft margin where two training errors are introduced to make data nonlinearly separable. Dotted examples are called the support vectors (they determine the margin by which the two classes are separated).