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Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification
2013
NeuroImage
Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a
doi:10.1016/j.neuroimage.2013.03.066
pmid:23583748
pmcid:PMC3767485
fatcat:3vb543ktejfnzh4hd4nlcndi4i