Solution path algorithm for the fuzzy weighted doubly regularized support vector machine

A fuzzy weighted doubly regularized support vector machine for binary classification is proposed in this paper. Fuzzy weights are presented by using the distance information within each class. Then the fuzzy weighted doubly regularized support vector machine is proposed by combing the weighted hinge loss and the adaptive elastic net penalty. A reasonable correlation between two model parameters is also given and the solution path algorithm to compute the solution paths of the proposed support vector machine is developed. The simulation results on two data sets demonstrate the effectiveness of the proposed method.