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From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature.
11 de sept. de 2023 · Let’s take a look at the goals of comparison: Better performance. The primary objective of model comparison and selection is definitely better performance of the machine learning software /solution. The objective is to narrow down on the best algorithms that suit both the data and the business requirements. Longer lifetime.
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief P.K. Douglasa,*, Sam Harrisb, Alan Yuillec, and Mark S. Cohena,b,d a Department of Biomedical Engineering, University of California, Los Angeles, USA b Interdepartmental Neuroscience Program, University of California, Los Angeles, USA c Department of ...
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 ...
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief P.K. Douglasa,*, Sam Harrisb, Alan Yuillec, and Mark S. Cohena,b,d a Department of Biomedical Engineering, University of California, Los Angeles, USA b Interdepartmental Neuroscience Program, University of California, Los Angeles, USA c Department of ...
15 de oct. de 2020 · From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature.
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief. NeuroImage , In Press, Uncorrected Proof. Elia Formisano, Federico De Martino, and Giancarlo Valente. Multivariate analysis of fMRI time series: classi cation and regression of brain responses using machine ...
