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.