Improvements to Bennett's Nearest Point Algorithm for Support Vector Machines

摘要

Intuitive geometric interpretation for Support Vector Machines (SVM) provides an alternative way to implement SVM. Although Bennett's nearest point algorithm (NPA) can deal with reduced convex hulls, it has some disadvantages. In the paper, a feasible direction explanation for NPA is proposed so that computation of kernel can be reduced greatly. Besides, the original NPA is extended to handle the arbitrary valid value of μ, therefore a better generalization performance may be obtained.