Machine-Learning Methods on Noisy and Sparse Data

Machine-Learning Methods on Noisy and Sparse Data

Experimental and computational data and field data obtained from measurements are often sparse and noisy. Consequently, interpolating unknown functions under these restrictions to provide accurate predictions is very challenging. This study compares machine-learning methods and cubic splines on the...

Full description

Bibliographic Details
Main Authors: Konstantinos Poulinakis, Dimitris Drikakis, Ioannis W. Kokkinakis, Stephen Michael Spottswood
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Mathematics
Subjects:
machine learning
deep neural networks
MARS
splines
interpolation
feedforward neural networks
Online Access:https://www.mdpi.com/2227-7390/11/1/236

Internet

https://www.mdpi.com/2227-7390/11/1/236

Similar Items