A Novel Smooth Support Vector Regression Based on CHKS Function
This paper presents a new smooth approach to solve support vector regression (SVR). Based on Karush-Kuhn-Tucker complementary condition in optimization theory, a smooth unconstrained optimization model for SVR is built. Since the objective function of the unconstrained SVR model is non-smooth, we apply the smooth techniques and replace the ε-insensitive loss function by CHKS function. Newton-Armijo algorithm is used to solve the smooth CHKS-SSVR model. Primary numerical results illustrate that our proposed approach improves the regression performance and the learning efficiency.
Q. Wu "A Novel Smooth Support Vector Regression Based on CHKS Function", Applied Mechanics and Materials, Vols. 44-47, pp. 3746-3751, 2011