TY  - JOUR
ID  - epos1329
UR  - http://dx.doi.org/10.1007/s10483-009-0113-x
IS  - 1
A1  - Zhou, Zhi-xiang
A1  - Han, Feng-qing
N2  - To improve performance of a support vector regression, a new method for a modi?ed 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 modi?ed 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. Simulation 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 ?gure of merit decreases and converges. The speed of convergence depends on the parameters used in the algorithm.
VL  - 30
TI  - An iterative modified kernel based on training data
AV  - none
EP  - 128
Y1  - 2009///
PB  - Springer Verlag
JF  - Applied Mathematics and Mechanics
KW  - support vector regression
KW  -  data-dependent
KW  -  kernel function
KW  -  iteration
SN  - 0253-4827
SP  - 121
ER  -