The SVM is a ML algorithm used for classification. The result of training an SVM is a hyperplane whose distance to the closest training instance, in either class, is maximal. Furthermore, instances from each class are required to reside on separate sides of the hyperplane. New instances are classified based solely on which side of the hyperplane they are located. The distance from the hyperplane to the closest training instance is known as the margin. The hyperplane that maximises the margin is the maximum-margin hyperplane and this is what is produced when training an SVM.