EEG signal classification via pinball universum twin support vector machine

DOI10.1007/S10479-022-04922-XzbMath1522.92032OpenAlexW4292367498WikidataQ114227553 ScholiaQ114227553MaRDI QIDQ6179181

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Publication date: 5 September 2023

Published in: Annals of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10479-022-04922-x

zbMATH Keywords

support vector machinetwin support vector machinepinball lossuniversumEEG signal classificationinterictal

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Biomedical imaging and signal processing (92C55) PDEs in connection with biology, chemistry and other natural sciences (35Q92)

Cites Work

• Angle-based twin support vector machine
• Support-vector networks
• Twin support vector machine with Universum data
• Large-scale pinball twin support vector machines
• General twin support vector machine with pinball loss function
• Efficient and robust TWSVM classification via a minimum L1-norm distance metric criterion
• Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec
• Least squares twin support vector machine with Universum data for classification

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