Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.
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A review of feature reduction techniques in neuroimagingThe utility of data-driven feature selection: re: Chu et al. 2012.Single trial decoding of belief decision making from EEG and fMRI data using independent components features.Evaluation of various machine learning methods to predict vision-related quality of life from visual field data and visual acuity in patients with glaucoma.Methods for cleaning the BOLD fMRI signal.Discriminating between glaucoma and normal eyes using optical coherence tomography and the 'Random Forests' classifierIdentifying areas of the visual field important for quality of life in patients with glaucoma.Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering.Diffusion Tensor Imaging of TBI: Potentials and Challenges.Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlatesCross-sectional study: Does combining optical coherence tomography measurements using the 'Random Forest' decision tree classifier improve the prediction of the presence of perimetric deterioration in glaucoma suspects?Bayesian networks in neuroscience: a survey.Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM.PredPsych: A toolbox for predictive machine learning-based approach in experimental psychology research.Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms.Root traits are more than analogues of leaf traits: the case for diaspore mass.Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis.Statistically reinforced machine learning for nonlinear patterns and variable interactions
P2860
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P2860
Performance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.
description
2010 nî lūn-bûn
@nan
2010 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Performance comparison of mach ...... oding of belief vs. disbelief.
@ast
Performance comparison of mach ...... oding of belief vs. disbelief.
@en
Performance comparison of mach ...... oding of belief vs. disbelief.
@nl
type
label
Performance comparison of mach ...... oding of belief vs. disbelief.
@ast
Performance comparison of mach ...... oding of belief vs. disbelief.
@en
Performance comparison of mach ...... oding of belief vs. disbelief.
@nl
prefLabel
Performance comparison of mach ...... oding of belief vs. disbelief.
@ast
Performance comparison of mach ...... oding of belief vs. disbelief.
@en
Performance comparison of mach ...... oding of belief vs. disbelief.
@nl
P2093
P2860
P1433
P1476
Performance comparison of mach ...... oding of belief vs. disbelief.
@en
P2093
Alan Yuille
Mark S Cohen
P K Douglas
P2860
P304
P356
10.1016/J.NEUROIMAGE.2010.11.002
P407
P50
P577
2010-11-10T00:00:00Z