A Novel Smooth Support Vector Machines for Classification and Regression

Novel smoothing function method for Support Vector Classification (SVC) and Support Vector Regression (SVR) are proposed and attempt to overcome some drawbacks of former method which are complex, subtle, and sometimes difficult to implement. First, used Karush-Kuhn-Tucker complementary condition in optimization theory, unconstrained non-differentiable optimization model is built Then the smooth approximation algorithm basing on differentiable function is given. Finally, the paper trains the data sets with standard unconstraint optimization method. This algorithm is fast and insensitive to initial point. Theory analysis and numerical results illustrate that smoothing function method for SVMs are feasible and effective.