Kernel methods and their derivatives: Concept and perspectives for the earth system sciences.

ABSTRACT: Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed directly thus making the kernels difficult to interpret. The aim of this work is to show that it is indeed possible to interpret the functions learned by various kernel methods as they can be intuitive despite their complexity. Specifically, we show that derivatives of these functions have a simple mathematical formulation, are easy to compute, and can be applied to various problems. The model function derivatives in kernel machines is proportional to the kernel function deri

SUBMITTER: Johnson JE 

PROVIDER: S-EPMC7595302 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature