Efficient kernel functions for the general regression and modified probabilistic neural networks

Four spherical kernel functions and two associated distance measures for the general regression and modified probabilistic neural networks are compared using four classification and four nonlinear filtering data sets. The standard Gaussian kernel is compared with three efficient functions: the tophat, triangle and a quadratic form kernel function. The standard Euclidean distance measure and more computationally efficient Hamming distance measure are also compared. The work shows that the computationally efficient combination of quadratic kernel and Hamming distance measure can produce comparable results with the traditional Gaussian kernel with Euclidean distance measure