Support Vector Machines (page 2 of 10)

The SVM algorithm finds a hyperplane that creates a boundary between the feature vectors and target that maximizes the margin. A hyperplane is a decision boundary (such as a point, a line, or a plane) that separates classes of data. The SVM algorithm attempts to construct a hyperplane that minimizes the cost function across all training observations using the hinge loss function.