New Solution Method to Smoothing Support Vector Machine with One Control Parameter Smoothing Function

Support vector machine (SVM) can be seen as, a special binary classification method. The original model is a quadratical programming with linear inequalities constraints. It is a very important issue that how to get the optimal solution of SVM model. In this paper, a new solution method is proposed. The constraints are moved away from the original optimization model by using the approximation solution in the feasible space. One control parameter smoothing function is used to smoothen the objective function of unconstrained model. The smoothing performance is investigated. By theory proof, the proposed unconstrained model has an active performance which can be controlled by one proposed parameter.