A novel model modification method for support vector regression based on radial basis functions

There are some inherent limitations to the performance of support vector regression (SVR), such as (i) the loss function, penalty parameter, and kernel function parameter usually cannot be determined accurately; (ii) the training data sometimes cannot be fully utilized; and (iii) the local accuracy in the vicinity of training points still need to be improved. To further enhance the performance of SVR, this paper proposes a novel model modification method for SVR with the help of radial basis functions. The core idea of the method is to start with an initial SVR and modify it in a subsequent stage to extract as much information as possible from the existing training data; the second stage does not require new points. Four types of modified support vector regression (MSVR), including MSVg, MSVm, MSVi, and MSVc, are constructed by using four different forms of basis functions. This paper evaluates the performances of SVR, MSVg, MSVm, MSVi, and MSVc by using six popular benchmark problems and a practical engineering problem, which is designing a typical turbine disk for an air-breathing engine. The results show that all the four types of MSVR perform better than SVR. Notably, MSVc has the best performance.