AIMC Topic: Earth Sciences
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Kernel methods and their derivatives: Concept and perspectives for the earth system sciences.
PloS one
Oct 29, 2020
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 conside...
Deep learning and process understanding for data-driven Earth system science.
Nature
Feb 13, 2019
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, ...
Electrical resistivity imaging inversion: An ISFLA trained kernel principal component wavelet neural network approach.
Neural networks : the official journal of the International Neural Network Society
Apr 24, 2018
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve ab...
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