Evaluation and comparision of compactly supported radial basis function for kernel machine

In order to reduce computer storage requirements for kernel matrix and the computational costs for floating point operations in kernel machine learning, compactly supported radial basis function is used for kernel machine to construct sparse kernel matrix. This paper deals with evaluation and comparison of compactly supported radial basis function for kernel machine in three aspects: the savings in storage, computation time for training, and performance. It is shown that savings in storage can be adjusted by user parameters, computation time for training decreases but it doest not mean that the more sparse the less training time, it will be stationary when ratio of non-zero elements of kernel matrix is in some range, the test accuracy to evaluate performance do not change much from our experimental results.