Total variation regularization for fMRI-based prediction of behavior.
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How machine learning is shaping cognitive neuroimaging.SCALABLE FUSED LASSO SVM FOR CONNECTOME-BASED DISEASE PREDICTIONBayesian multi-task learning for decoding multi-subject neuroimaging data.A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data.Longitudinal deformation models, spatial regularizations and learning strategies to quantify Alzheimer's disease progression.Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine.Brain Imaging AnalysisCortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm.Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.The relevance voxel machine (RVoxM): a self-tuning Bayesian model for informative image-based predictionFunctional brain networks reconstruction using group sparsity-regularized learning.Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.A decomposition model and voxel selection framework for fMRI analysis to predict neural response of visual stimuli.Real-time estimation of dynamic functional connectivity networks.Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis.Sparsity Is Better with Stability: Combining Accuracy and Stability for Model Selection in Brain Decoding.Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects.Generalized Scalar-on-Image Regression Models via Total Variation.MWPCR: Multiscale Weighted Principal Component Regression for High-dimensional Prediction.A Comparative Study of Algorithms for Intra- and Inter-subjects fMRI DecodingBeyond Brain Reading: Randomized Sparsity and Clustering to Simultaneously Predict and IdentifyEarly Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine LearningConvex relaxations of penalties for sparse correlated variables with bounded total variation
P2860
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P2860
Total variation regularization for fMRI-based prediction of behavior.
description
2011 nî lūn-bûn
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2011年の論文
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2011年学术文章
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2011年学术文章
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2011年学术文章
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2011年学术文章
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2011年学术文章
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2011年學術文章
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2011年學術文章
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2011年學術文章
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name
Total variation regularization for fMRI-based prediction of behavior.
@en
Total variation regularization for fMRI-based prediction of behavior.
@nl
type
label
Total variation regularization for fMRI-based prediction of behavior.
@en
Total variation regularization for fMRI-based prediction of behavior.
@nl
prefLabel
Total variation regularization for fMRI-based prediction of behavior.
@en
Total variation regularization for fMRI-based prediction of behavior.
@nl
P2860
P50
P356
P1476
Total variation regularization for fMRI-based prediction of behavior.
@en
P2093
Vincent Michel
P2860
P304
P356
10.1109/TMI.2011.2113378
P577
2011-02-10T00:00:00Z