SPLINE FUNCTION SMOOTH SUPPORT VECTOR MACHINE FOR CLASSIFICATION
Support vector machine (SVM) is a very popular method for binary data classification in data mining (machine learning) Since the objective function of the unconstrained SVM model is a non smooth function, a lot of good optimal algorithms can't be used to find the solution. In order to overcome this model's non smooth property, Lee and Mangasarian proposed smooth support vector machine (SSVM) in 2001. Later, Yuan et al. proposed the polynomial smooth support vector machine (PSSVM) in 2005. In this paper, a three order spline function is used to smooth the objective function and a three order spline smooth support vector machine model (TSSVM) is obtained. By analyzing the performance of the smooth function, the smooth precision has been improved obviously. Moreover, BFGS and Newton Armijo algorithms are used to solve the TSSVM model. Our experimental results prove that the TSSVM model has better classification performance than other competitive baselines.