Classification Analytics in Functional Neuroimaging: Calibrating Signal Detection Parameters
Classification analyses are a promising way to localize signal, especially scattered signal, in functional magnetic resonance imaging data. However, there is not yet a consensus on the most effective analysis pathway. We explore the efficacy of k-Nearest Neighbors classifiers on simulated functional...
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| Language: | en_US |
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The University of Arizona.
2015
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| Online Access: | http://hdl.handle.net/10150/594646 |