An iterative modified kernel based on training data  

作　　者：周志祥 韩逢庆 

机构地区：[1]College of Civil Engineering and Architecture, Chongqing Jiaotong University,Chongqing 400074, P. R. China [2]School of Science, Chongqing Jiaotong University,Chongqing 400074, P. R. China

出　　处：《Applied Mathematics and Mechanics(English Edition)》2009年第1期121-128,共8页应用数学和力学（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (No. 50578168);the Natural Science Foundation of CQ CSTC (No. 2007BB2396)

摘　　要：To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.To improve performance of a support vector regression, a new method for a modified kernel function is proposed. In this method, information of all samples is included in the kernel function with conformal mapping. Thus the kernel function is data-dependent. With a random initial parameter, the kernel function is modified repeatedly until a satisfactory result is achieved. Compared with the conventional model, the improved approach does not need to select parameters of the kernel function. Sim- ulation is carried out for the one-dimension continuous function and a case of strong earthquakes. The results show that the improved approach has better learning ability and forecasting precision than the traditional model. With the increase of the iteration number, the figure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.

关 键 词：support vector regression data-dependent kernel function ITERATION 

分 类 号：N945.24[自然科学总论—系统科学]