Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data

Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer...

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Bibliographic Details
Main Author:	Perez, Daniel Antonio
Published:	Georgia Institute of Technology 2010
Subjects:	Pattern recognition Support vector machines Relevance vector machines Machine learning FMRI Diagnostic imaging Magnetic resonance imaging Imaging systems in medicine Supervised learning (Machine learning)
Online Access:	http://hdl.handle.net/1853/34858

id	ndltd-GATECH-oai-smartech.gatech.edu-1853-34858
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spelling	ndltd-GATECH-oai-smartech.gatech.edu-1853-348582013-08-17T03:08:50ZPerformance comparison of support vector machine and relevance vector machine classifiers for functional MRI dataPerez, Daniel AntonioPattern recognitionSupport vector machinesRelevance vector machinesMachine learningFMRIDiagnostic imagingMagnetic resonance imagingImaging systems in medicineSupervised learning (Machine learning)Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.Georgia Institute of Technology2010-09-15T19:12:30Z2010-09-15T19:12:30Z2010-07-12Thesishttp://hdl.handle.net/1853/34858
collection	NDLTD
sources	NDLTD
topic	Pattern recognition Support vector machines Relevance vector machines Machine learning FMRI Diagnostic imaging Magnetic resonance imaging Imaging systems in medicine Supervised learning (Machine learning)
spellingShingle	Pattern recognition Support vector machines Relevance vector machines Machine learning FMRI Diagnostic imaging Magnetic resonance imaging Imaging systems in medicine Supervised learning (Machine learning) Perez, Daniel Antonio Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
description	Multivariate pattern analysis (MVPA) of fMRI data has been growing in popularity due to its sensitivity to networks of brain activation. It is performed in a predictive modeling framework which is natural for implementing brain state prediction and real-time fMRI applications such as brain computer interfaces. Support vector machines (SVM) have been particularly popular for MVPA owing to their high prediction accuracy even with noisy datasets. Recent work has proposed the use of relevance vector machines (RVM) as an alternative to SVM. RVMs are particularly attractive in time sensitive applications such as real-time fMRI since they tend to perform classification faster than SVMs. Despite the use of both methods in fMRI research, little has been done to compare the performance of these two techniques. This study compares RVM to SVM in terms of time and accuracy to determine which is better suited to real-time applications.
author	Perez, Daniel Antonio
author_facet	Perez, Daniel Antonio
author_sort	Perez, Daniel Antonio
title	Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
title_short	Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
title_full	Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
title_fullStr	Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
title_full_unstemmed	Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data
title_sort	performance comparison of support vector machine and relevance vector machine classifiers for functional mri data
publisher	Georgia Institute of Technology
publishDate	2010
url	http://hdl.handle.net/1853/34858
work_keys_str_mv	AT perezdanielantonio performancecomparisonofsupportvectormachineandrelevancevectormachineclassifiersforfunctionalmridata
_version_	1716596227542351872

Performance comparison of support vector machine and relevance vector machine classifiers for functional MRI data

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