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15 mag 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.
- Pamela K. Douglas, Sam Harris, Alan L. Yuille, Mark S. Cohen
- 2011
15 mag 2011 · NeuroImage. Volume 56, Issue 2, 15 May 2011, Pages 544-553. Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. P.K. Douglas a. , Sam Harris b. , Alan Yuille c. , Mark S. Cohen a b d. Show more. Add to Mendeley.
- Pamela K. Douglas, Sam Harris, Alan L. Yuille, Mark S. Cohen
- 2011
15 mag 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.
- Pamela K. Douglas, Sam Harris, Alan L. Yuille, Mark S. Cohen
- 2011
1 nov 2010 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. November 2010....
Fig. 6. Methodology for projecting highly ranked IC spatial maps forward onto. - "Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief"
NeuroImage 56 (2011) 544–553 Contents lists available at ScienceDirect NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief P.K. Douglas a,⁎, Sam Harris b, Alan Yuille c, Mark S. Cohen a,b,d a Department of ...
Classification accuracy averaged across all subjects, shown for each of the six classifiers as a function of the number of ICs, with fits to 3-parameter first order exponential model (lines). - "Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief"
