Abstract: Iteratively reweighted method is an important approach to improve the robustness of least sctuares support vector machine(LS-SVM). However, the reweighting and retraining procedure demands a lot of computational time, which makes it impossible for practical applications. In this paper, the iteratively reweighted least squares support vector machine (IRLS-SVM) was studied. An improved training algorithm of IRLS-SVM was proposed. It is based on novel numerical method, and can effectively reduce the computational complexity of IRIS-SVM. Three different weight funclions were implemented in the IRLS-SVM. Experiments on simulated instances and real-world datasets demonstrate the validity of this algorithm. Meanwhile, the results reveal that different weight function may require different computational time for the fast training algorithm of IRLS-SVM.

Key words: Support vector machines,Robustness,Outliers,Fast algorithm