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APPLICATION OF SUPPORT VECTOR MACHINES TO LONGITUDINAL FUNCTIONAL NEUROIMAGING DATA

dc.contributor.authorRudiuk, Alexander
dc.contributor.copyright-releaseNot Applicableen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Biomedical Engineeringen_US
dc.contributor.ethics-approvalReceiveden_US
dc.contributor.external-examinerDr. Jeremy Brownen_US
dc.contributor.graduate-coordinatorDr. Janie Wilsonen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerDr. Robert Adamsonen_US
dc.contributor.thesis-readerDr. Thomas Trappenbergen_US
dc.contributor.thesis-supervisorDr. Steven Beyeaen_US
dc.contributor.thesis-supervisorDr. Tim Bardouilleen_US
dc.date.accessioned2016-12-15T15:39:12Z
dc.date.available2016-12-15T15:39:12Z
dc.date.defence2016-11-22
dc.date.issued2016-12-15T15:39:12Z
dc.description.abstractThe principal objective of this thesis was to test a novel adaptation of the support vector machine (SVM), called a longitudinal support vector machine(LSVM), on longitudinal functional neuroimaging data. LSVM performance was compared to a traditional SVM and logistic regression (LR) using classification accuracy and interpretability of feature weights. Classification accuracy was measured as the percentage of subjects placed into their correct categories, and feature weights by how closely they matched the known signal. The first study involved purely simulated data, which found the LSVM had higher classification accuracy for data without heteroscedasticity, but performed worse when heteroscedasticity was introduced. The second study used real magnetoencephalography (MEG) resting state readings added to a simulated trend. The LSVM had similar classification accuracy, and only had more interpretable feature weights at the highest SNR dataset. Currently the LSVM is not recommended over the SVM/LR algorithms.en_US
dc.identifier.urihttp://hdl.handle.net/10222/72586
dc.language.isoenen_US
dc.subjectMachine Learningen_US
dc.subjectFunctional Longitudinal Neuroimaging Dataen_US
dc.subjectMagnetoencephalographyen_US
dc.subjectSupport Vector Machineen_US
dc.titleAPPLICATION OF SUPPORT VECTOR MACHINES TO LONGITUDINAL FUNCTIONAL NEUROIMAGING DATAen_US

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