Predicting the Total Workload in Telecommunications by SVMs

As a learning mechanic, support vector machine (SVMs) has been studied and applied in a wide area. This study deals with the special futures of SVM in predicting the total workload in telecommunication. The contributions include: (a) Building a predicted model of the total workload in telecommunications and predicting using it; (b)Analyzing the parameter of support vector regression(SVRs) which influence performance of SVRs. (c) Experiments demonstrate that SVM in this paper outperforms the others methods in this area.