Effects of norms on learning properties of support vector machines

Support vector machines (SVMs) are known to have a high generalization ability, yet a heavy computational load since margin maximization results in a quadratic programming problem. It is known that this maximization task results in a pth-order programming problem if we employ the L_P norm instead of the L₂ norm. In this paper, we theoretically show the effects of p on the learning properties of SVMs by clarifying its geometrical meaning.