An appointment with Reproducing Kernel Hilbert Space generated by
  Generalized Gaussian RBF as $L^2-$measure
Himanshu Singh
Gaussian Radial Basis Function (RBF) Kernels are the most-often-employed
kernels in artificial intelligence and machine learning routines for providing
optimally-best results in contrast to their respective counter-parts. However,
a little is known about the application of the Generalized Gaussian Radial
Basis Function on various machine learning algorithms namely, kernel
regression, support vector machine (SVM) and pattern-recognition via neural
networks. The results that are yielded by Generalized Gaussian RBF in the
kernel sense outperforms in stark contrast to Gaussian RBF Kernel, Sigmoid
Function and ReLU Function. This manuscript demonstrates the application of the
Generalized Gaussian RBF in the kernel sense on the aforementioned machine
learning routines along with the comparisons against the aforementioned
functions as well.