A tight support kernel for support vector machine

By comparing performance of common kernels and wavelet kernels in classification, criterion of effective kernel for support vector classifier is concluded, thereby a tight support kernel is constructed by smoothing Shannon scaling function in Fourier domain and combining with spline function. Experiment results indicate that the proposed kernel has faster training speed and higher accuracy than Gaussian kernel on some Benchmark UCI classification datasets, hence reconfirm the criterion.