@article {IOPORT.05733510,
    author       = {Du, Zhe and Liu, Sanyang and Wu, Qing},
    title        = {A direct support vector machine for regression.},
    year         = {2009},
    journal      = {Systems Engineering and Electronics},
    volume       = {31},
    number       = {1},
    issn         = {1001-506X},
    pages        = {178-181},
    publisher    = {Editorial Department of Systems Engineering and Electronics, Beijing},
    abstract     = {Summary: A direct support vector machine for regression (DSVMR) is obtained when appending the square bias of the hyper-plane into the object function in a least square support vector machine for regression (LSSVMR). Compared with LSSVMR, the DSVMR strengthens the convexity of the problem to be solved and only needs to calculate the inversion of a symmetrical positive definite matrix which is similar to a kernel matrix. The Cholesky decomposing and the SMW (Sherman-Morrison-Woodbury) formula are employed to reduce the computing amount greatly, accelerate the learning speed and have no weakening approximate ability. Numerical experiments show that the new method is feasible and has the above advantages.},
    identifier   = {05733510},
}