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The LS_SVM classifier is used to classify two-category EEG data after the feature extraction and selection.
Moreover, the Topology Classifier algorithm is used to classify the constructed molecules as drugs or non-drugs.
One of the most popular classifiers, the least square support vector machines (LS_SVMs) [11], is used to classify EEG data.
Meanwhile, Softmax regression (SR) is used to classify expression feature.
Then GMM is used to classify the numerous spectral parameters.
First, the interactor is used to classify multiinput-multioutput processes according to their delay structure.
Finally, the SVM-based pattern recognition model is used to classify different ECG patterns.
Finally, support vector machine (SVM) is used to classify different fault patterns.
A binary decision tree is used to classify the extracted features.
The nearest neighbor rule is used to classify any image projection to the known RBC coordinates.
Homotopy theory is used to classify a topological type of a beam structure.
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