The above intuition can be generalized to more than two dimensions, allowing
multiple attributes to be used for classification; in this case, the classifier finds a
dividing plane, not a line. Further, by first transforming the input points using
certain functions, called kernel functions, Support Vector Machine classifiers can
find nonlinear curves separating the sets of points.