首页|Data on Support Vector Machines Discussed by Researchers at University of Baghda d (EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier)
Data on Support Vector Machines Discussed by Researchers at University of Baghda d (EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Researchers detail new data in support vector mac hines. According to news originating from the University of Baghdad by NewsRx ed itors, the research stated, “This work implements an Electroencephalogram (EEG) signal classifier.” The news editors obtained a quote from the research from University of Baghdad: “The implemented method uses Orthogonal Polynomials (OP) to convert the EEG sign al samples to moments. A Sparse Filter (SF) reduces the number of converted mome nts to increase the classification accuracy. A Support Vector Machine (SVM) is u sed to classify the reduced moments between two classes. The proposed method’s p erformance is tested and compared with two methods by using two datasets. The da tasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this met hod overcomes the accuracy of other methods.”
University of BaghdadMachine LearningMathematicsPolynomialSupport Vector Machines
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