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Machine Learning for Neuroimaging with Scikit-Learn
[article]
2014
arXiv
pre-print
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find
arXiv:1412.3919v1
fatcat:fiepevd7gzbl3ecit47e7onl6m