An effective way to integrate ε-support vector regression with gradients

ε-support vector regression (ε-SVR), as a direct implementation of the structural risk minimization principle rather than empirical risk minimization principle, is a new regression method with good generalization ability and can efficiently solve small-sample learning problems. In this work, through incorporating gradient information into the traditional ε-SVR, the gradient-enhanced ε-SVR (GESVR) is developed. The efficiency of GESVR is compared with the traditional ε-SVR by employing analytical function fitting, compared with the gradient-enhanced least square support vector regression (GELSSVR) by using two real-life examples, and tested in a scenario where the exact gradient information is unknown. The results show that GESVR provides more accurate prediction results than the traditional ε-SVR model, and outperforms GELSSVR in some real-life cases.