Oblique support vector machines

We propose a modified framework of support vector machines, called oblique support vector machines (OSVMs), to improve the capability of classification. The principle of OSVMs is joining an orthogonal vector into a weight vector in order to rotate the support hyperplanes. Thus, not only the regularized risk function is revised, but the constrained functions are also modified. Under this modification, the separating hyperplane and the margin of separation are constructed more precisely. Consequently, experimental results are given to demonstrate that the performance of OSVMs is better than that of SVMs and SSVMs.