Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

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small sample and nonlinear classification problems support vector machines motor imagery eeg signals classification accuracy particle swarm optimization algorithm kernel and penalty parameters classifier

Xiaohui Ding,Thomas Potter,.Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization. 2016 (),.

Xiaohui Ding,Thomas Potter,"Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization" 2016

Xiaohui Ding,Thomas Potter,"Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization"