Too much information is no information: how machine learning and feature selection could help in understanding the motor control of pointing
The aim of this study was to develop the use of Machine Learning techniques as a means of multivariate analysis in studies of motor control. These studies generate a huge amount of data, the analysis of which continues to be largely univariate. We propose the use of machine learning classification a...
Main Authors: | Elizabeth Thomas, Ferid Ben Ali, Arvind Tolambiya, Florian Chambellant, Jérémie Gaveau |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-07-01
|
Series: | Frontiers in Big Data |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2023.921355/full |
Similar Items
-
Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
by: Gomathy Ramaswami, et al.
Published: (2022-09-01) -
mSHAP: SHAP Values for Two-Part Models
by: Spencer Matthews, et al.
Published: (2021-12-01) -
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification
by: Daniel Probst
Published: (2023-11-01) -
Distributional Prototypical Methods for Reliable Explanation Space Construction
by: Hyungjun Joo, et al.
Published: (2023-01-01) -
Explainable machine learning for public policy: Use cases, gaps, and research directions
by: Kasun Amarasinghe, et al.
Published: (2023-01-01)