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Resumen de Grey wolf optimization based parameter selection for support vector machines

Sathish Eswaramoorthy, N. Sivakumaran, Sankaranarayanan Sekaran

  • Purpose – The purpose of this paper is to tune support vector machine (SVM) classifier using grey wolf optimizer (GWO).

    Design/methodology/approach – The schema of the work aims at extracting the features from the collected data followed by a SVM classifier and metaheuristic optimization to tune the classifier parameters.

    Findings – The optimal tuning of classifier parameters lowers errors due to manual elucidation and decreases the risk in human perceptions and repeated visual dignosis.

    Originality/value – A novel, GWO based tuning algorithm is used for SVM classifier, which is implemented in classifying the complex and nonlinear biomedical signals like intracranial electroencephalogram


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