Fig. 3.

An illustrative example of support vector machines. The goal of the support vector machines algorithm is to find the hyperplane that maximizes the separation of features. The solid black line represents the optimal hyperplane, whereas the dotted lines represent the planes running through the support vectors. The empty circle and the solid triangle represent support vectors—the data points from each cluster that represent the closest points to the optimal hyperplane. The dashed line represents the maximum margin between the support vectors.