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Classifier combination has become a very important topic, because it is possible to train many classifiers using different feature, instance subsets or different types of classifiers. Classifier diversity and accuracy are two competing requirements for classifier combination. In this paper, we study classifier combination using the kernelized eigenclassifiers The eigenclassifier method, tries to handle...
In this paper, a large number of features are extracted from raw EEG data and then feature selection and classification are performed ,for brain computer interface (BCI) applications using motor imaginary movements. As the feature selection method, mRMR (minimum Redundancy Maximum Relevance) method, which is a fast method to select relevant and non redundant feature set, is chosen. Using a number...
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