Abstract: The selection of kernel function and its parameters plays a significant role in support vector machine (SVM) classification algorithms．Based on Hermite polynomial and the triangular kernel function,a new set of kernel functions—triangular Hermite kernel was proposed．The triangular Hermite kernel has two parameters．One parameter is determined by the distance between sample points and sample mean,and the other parameter is chosen only from nonnegative integer．So the parameters of the triangular Hermite kernel can be optimized easily．The experimental results on bi-spiral data,checkerboard data and 7UCI data sets indicate that the new kernel achieves the competitive classification performance,compared with polynomial kernel,Gaussian kernel,and the previous Hermite kernel proposed in reference [6]．

Key words: Support vector machine,Kernel selection,Hermite polynomial,Triangular kernel function