Efficient twin parametric insensitive support vector regression model

In this paper, an efficient twin parametric insensitive support vector regression (TPISVR) is proposed. The TPISVR determines indirectly the regression function through a pair of nonparallel parametric-insensitive up- and down-bound functions solved by two smaller sized support vector machine (SVM)-type problems, which causes the TPISVR not only have the faster learning speed than the classical SVR, but also be suitable for many cases, especially when the noise is heteroscedastic, that is, the noise strongly depends on the input value. The proposed method has the advantage of using the ratio of the parameters νν and c for controlling the bounds of fractions of support vectors and errors. The experimental results on several artificial and benchmark datasets indicate that the TPISVR not only has fast learning speed, but also shows good generalization performance.