Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition

In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test...

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Vydané v:	Frontiers in neuroscience Ročník 16; s. 855421
Hlavní autori:	Dan, Yufang, Tao, Jianwen, Zhou, Di
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 04.05.2022 Frontiers Media S.A
Predmet:	Accuracy Adaptive learning Classification clustering assumption Datasets EEG Efficiency Electroencephalography emotion recognition Emotions encephalogram Entropy fuzzy entropy Machine learning multi-model adaptation Neuroscience Noise Remote sensing semi-supervised learning encephalogram fuzzy entropy semi-supervised learning multi-model adaptation clustering assumption emotion recognition
ISSN:	1662-453X, 1662-4548, 1662-453X
On-line prístup:	Získať plný text
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