2020 6th IEEE Congress on Information Science and Technology (CiSt) 2020
DOI: 10.1109/cist49399.2021.9357209
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BCI: Classifiers Optimization for EEG Signals Acquiring in Real-Time

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Cited by 3 publications
(5 citation statements)
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“…Traditional artificial neural networks that are based on the lowest empirical risk outperform the SVM since it is based on the smallest structural risk. This classifier's purpose is to find the best hyperplane for distinguishing each mode class [1,2,3]. The SVM chooses hyperplanes that group the most points of the same class together while keeping the gap between each class and those hyperplanes as little as possible.…”
Section: Support Vector Machine (Svm)mentioning
confidence: 99%
See 4 more Smart Citations
“…Traditional artificial neural networks that are based on the lowest empirical risk outperform the SVM since it is based on the smallest structural risk. This classifier's purpose is to find the best hyperplane for distinguishing each mode class [1,2,3]. The SVM chooses hyperplanes that group the most points of the same class together while keeping the gap between each class and those hyperplanes as little as possible.…”
Section: Support Vector Machine (Svm)mentioning
confidence: 99%
“…The support vector is created using the hyperplane's nearest point. The shortest path between them and class points is the distance from the class to the hyperplane [1,2,3]. This is how far something is measured.…”
Section: Support Vector Machine (Svm)mentioning
confidence: 99%
See 3 more Smart Citations