Machine Learning Application of Generalized Gaussian Radial Basis Function and Its Reproducing Kernel Theory
Gaussian Radial Basis Function Kernels are the most-often-employed kernel function in artificial intelligence for providing the optimal results in contrast to their respective counterparts. However, our understanding surrounding the utilization of the <i>Generalized Gaussian Radial Basis Funct...
Main Author: | Himanshu Singh |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-03-01
|
Series: | Mathematics |
Subjects: | |
Online Access: | https://www.mdpi.com/2227-7390/12/6/829 |
Similar Items
-
A dynamic state estimation method for integrated energy system based on radial basis kernel function
by: Chen, Tengpeng, et al.
Published: (2024) -
Meshless Galerkin method based on RBFs and reproducing Kernel for quasi-linear parabolic equations with dirichlet boundary conditions
by: Mehdi Mesrizadeh, et al.
Published: (2021-05-01) -
Learnable Leaky ReLU (LeLeLU): An Alternative Accuracy-Optimized Activation Function
by: Andreas Maniatopoulos, et al.
Published: (2021-12-01) -
Learning With Multiple Kernels
by: Mahdi A. Almahdawi, et al.
Published: (2024-01-01) -
Optimization of Microchannels and Application of Basic Activation Functions of Deep Neural Network for Accuracy Analysis of Microfluidic Parameter Data
by: Feroz Ahmed, et al.
Published: (2022-08-01)