Abstract
Support Vector Machine (SVM) is a new classification technique which has a high generalization ability, however, it requires a heavy computational load since margin maximization results in a quadratic program. In this article, we show that the task results in a p-th order program if we employ the Lp norm instead of the L2 norm. This means that we can reduce the computational load by using the L1 norm. A geometrical view of SVM with the Lp norm explains that the change of p little affects the generalization performance of SVM in practice. This is confirmed by computer simulations.
