As shown in a previous study , the GRBF
kernel tends to achieve better segmentation results on simulated MRI images corrupted by noise and other artifacts than polynomial kernel.
It's observed from the result that Gaussian radial basis kernel function (GRBF) is giving better result when compared to other kernel functions.
The proposed CAD system includes pre-processing by WTHE and feature extraction by GFWHT and SVM by GRBF. In first step, the performance of WTHE is better than WFMF by 42% and WFWT by 34.55% and WFHE by 32.11% and MFWT by 26% and MFHE by 24.21%.
 are obtained from GB by introducing an extra empirical parameter, with the aim to control the beam width at a given distance from the source.
From Table 4 the polynomial kernel and GRBF
kernel give better results.
Mulero-Martinez  proposed anew Gaussian radial basis function (GRBF
) static neurocontroller, which is a two-stage controller acting in a supervisory fashion by means of a switching logic and allowing arbitration between a neural network (NN) and a robust proportionalderivative controller.
(iii) For the RLSTD algorithm, the feature vector [phi](s) consists of 19 Gaussian radius basis functions (GRBFs
) plus a constant term 1, resulting in a total of 20 basis functions.