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Abstract #1144

A Comparison of SVM and RVM for Real-Time FMRI Applications

Daniel Antonio Perez1, Richard Cameron Craddock2, George Andrew James1, Xiaoping Philip Hu1

1The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University, Atlanta, GA, United States; 2School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta,, GA, United States

Support vector machines (SVM) and relevance vector machines (RVM) are two machine learning algorithms which have gained popularity due to its sensitivity to networks of brain activation. Despite their recent extensive use in fMRI research, little contribution has been put forth to compare these different algorithms. Both models were compared for speed and prediction accuracy. The results revealed that both RVM and SVM are comparable in classification accuracy. However, RVM is capable of performing the task much faster and with a sparser model. Feature selection was also found to increase both speed and classification accuracy for both SVM and RVM.