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15. Mai 2011 · Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief - ScienceDirect. 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
- Pamela K. Douglas, Sam Harris, Alan L. Yuille, Mark S. Cohen
- 2011
15. Mai 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
5. Mai 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.
- Pamela K. Douglas, Sam Harris, Alan L. Yuille, Mark S. Cohen
- 10.1016/j.neuroimage.2010.11.002
- 2011
- 2011/05/05
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. Vasileios Vlachos. 2011, NeuroImage. See Full PDF. Download PDF.
- Vasileios Vlachos
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"
Fig. 4. 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"
Fig. 5. Rise threshold criterion applied to AdaBoost (left) and Naïve Bayes (right). - "Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief"
