An approach for kernel selection based on data distribution

This paper presents a data based kernel selection approach, which utilizes the geometry distribution of data. Once the approximate distribution can be confirmed as a special one like circle, cirque, sphere cylinder, et al, some known kernel functions corresponding to the special distribution can then be used. Four datasets are used to verify the presented approach, and simulation results demonstrate the rationality and effectiveness of the presented approach.