Kernel techniques: From machine learning to meshless methods
Year: 2006
Type: article
Abstract: Kernels are valuable tools in various fields of numerical analysis, including approximation, interpolation, meshless methods for solving partial differential equations, neural networks, and machine learning. This contribution explains why and how kernels are applied in these disciplines. It uncovers the links between them, in so far as they are related to kernel techniques. It addresses non-expert readers and focuses on practical guidelines for using kernels in applications.
Source: Acta Numerica
Authors: Robert Schaback, Holger Wendland
Institution: University of Göttingen
Language: English
Subfield: Mechanics of Materials
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2089245375