Predicting motor tasks in fMRI data with support vector machines (bibtex)
by S. LaConte, S. Strother, V. Cherkassky, X. Hu
Abstract:
The support vector machine (SVM) is introduced to fMRI as a method for classifying temporal scans. The SVM is found to perform comparably to canonical variates analysis (CVA) in terms of misclassification error. We examine the interpretation of the SVM model in the context of fMRI, and find that removing model-related scans enhances the statistical difference between those scans.
Reference:
abstract S. LaConte, S. Strother, V. Cherkassky, X. Hu. Predicting motor tasks in fMRI data with support vector machines. In Proceedings 11th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto, page 494, 2003. [bibtex]
Bibtex Entry:
@inproceedings{Toronto494,
   Author = {LaConte, S. and Strother, S. and Cherkassky, V. and Hu, X.},
   Title ={Predicting motor tasks in f{M}{R}{I} data with support vector machines},
   BookTitle = {Proceedings 11th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Toronto},
   Pages = {494},
   Abstract = {The support vector machine (SVM) is introduced to fMRI as a method for classifying temporal scans. The SVM is found to perform comparably to canonical variates analysis (CVA) in terms of misclassification error. We examine the interpretation of the SVM model in the context of fMRI, and find that removing model-related scans enhances the statistical difference between those scans.},
 Keywords = {Toronto494},
   Year = {2003} }
Powered by bibtexbrowser