An effective method for weighted support vector regression based on sample simplification

For large data-set, the speed of algorithms for support vector machines is one restriction for their performances; besides, noise and outliers which the data-set contained also influenced their capabilities greatly. This paper proposes an effective method for weighted support vector regression (SWSVR): the simplification of the original samples (contain noise and outliers) is taken firstly; then, the selected samples are trained by the weighted support vector regression machine. The results of the experiment shows that our method not only speedups the calculation, but also meliorates the performance of the regression.