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15 de may. de 2011 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. Neuroimage. 2011 May 15;56 (2):544-53. doi: 10.1016/j.neuroimage.2010.11.002. Epub 2010 Nov 10. Authors. P K Douglas 1 , Sam Harris , Alan Yuille , Mark S Cohen. Affiliation.
5 de may. de 2011 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief - PMC. Journal List. HHS Author Manuscripts. PMC3099263. As a library, NLM provides access to scientific literature.
15 de may. de 2011 · We apply pattern classifiers towards decoding belief cognitive states from fMRI. Independent Component (IC) Analysis was used for dimension reduction. Six ICs formed a diagnostic, parsimonious feature subset. Informative IC spatial masks were mapped forward. Classification accuracy of belief vs. disbelief was robust and varied by ...
15 de may. de 2011 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief - ScienceDirect. Abstract. Cited by (88) NeuroImage. Volume 56, Issue 2, 15 May 2011, Pages 544-553.
1 de nov. de 2010 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. November 2010....
10 de nov. de 2010 · Maximum accuracy was achieved at 92% for Random Forest, followed by 91% for AdaBoost, 89% for Naïve Bayes, 87% for a J48 decision tree, 86% for K*, and 84% for support vector machine. For real-time decoding applications, finding a parsimonious subset of diagnostic ICs might be useful.
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief (PDF) Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief | Mark Cohen - Academia.edu
