The support vectors. An SVM is a sparse kernel machine ; the decision rule in eqn. 21 will depend on a subset of the training data, the support vectors φ (L) (G n ) with α n > 0 that lie inside or on the boundary of the margin or outside the margin but on the wrong side of the decision boundary.