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  1. pubmed.ncbi.nlm.nih.gov › 21073969Performance comparison of machine learning algorithms and number...

    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.

  2. www.sciencedirect.com › science › articlePerformance comparison of machine learning algorithms and number...

    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 ...

  3. www.ncbi.nlm.nih.gov › pmc › articlesPerformance comparison of machine learning algorithms and number...

    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.

  4. www.sciencedirect.com › science › articlePerformance comparison of machine learning algorithms and number...

    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.

  5. www.researchgate.net › publication › 47789402Performance comparison of machine learning algorithms and number...

    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....

  6. europepmc.org › article › MEDPerformance comparison of machine learning algorithms and number...

    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.

  7. www.academia.edu › 54903658 › Performance_comparison_of_machine_learningPerformance comparison of machine learning algorithms and number...

    (PDF) Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief | samantha harris - Academia.edu. Download Free PDF. Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. samantha harris.