Support vector classification(SVC) provides more complete description of the linear and nonlinear relationships between input vectors and classifiers. In this paper we propose to solve the optimization problem of SVC with a modified hinge loss function, which enables to use an iterative reweighted least squares(IRWLS) procedure. We also introduce the approximate cross validation function to select the hyperparameters which affect the performance of SVC. Experimental results are then presented which illustrate the performance of the proposed procedure for classification.