Heuristic sample reduction based support vector regression method

Support vector regression (SVR) has become one of the most promising methods for function approximation and regression estimation. However, SVR has a time complexity of O(N
³
) and a space complexity of O(N
²
). When dealing with very large sizes of training sets, SVR takes a lot computational time. To solve this problem, a method called heuristic sample reduction (HSR) is proposed for obtaining a reduced training set that is manageable for SVR. HSR maintains the regression accuracy of SVR by building the reduced training set heuristically with the samples selected from the original. The experimental result shows that the method is very effective.