In this work, different kernels namely linear kernel, polynomial kernel, sigmoid, and radial basis function (RBF) kernel were used, and the effects of these kernel functions on SVR model based on GS-optimization method are summarized in Table 6.
The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI and Global Environmentt Monitoring Index (GEMI), respectively.
The radial basis function kernel is used.
In this research, the LS_SVM classifier with a radial basis function kernel is used for the classification of epileptic EEG signals.
For the SVM the Gaussian (radial basis function) kernel was used, as it has some advantages compared to the linear SVM and others.
The radial basis function kernel is used to transform the data into a higher dimensional space in order to be able to perform the separation in the non-linear region, while Naïve Bayes classifier is based on the Bayes' theorem with independence assumptions between predictors.
The radial basis function kernel was used.
When performing classification using the SVM classifier, because x is a combination of different features of a peptide, RBF (Radial Basis Function) kernel was used.
A radial basis function (RBF) kernel was used for classification.
Following the results given in [17], a Gaussian radial basis function (RBF) kernel is used as the SVM kernel function.
