Bayesian multi-task learning for decoding multi-subject neuroimaging data

NeuroImage

Volumn 92, Issue , 2014, Pages 298-311

  Marquand, Andre F ^a   Brammer, Michael ^a   Williams, Steven C R ^a   Doyle, Orla M ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Decoding; Functional magnetic resonance imaging; Gaussian process; Machine learning; Mixed effects; Multi output learning; Multi task learning; Pattern recognition; Repeated measures; Transfer learning

Indexed keywords

ACCURACY; ANALYTICAL PARAMETERS; ARTICLE; BAYESIAN LEARNING; BRAIN MAPPING; DATA PROCESSING; DECODING; FUNCTIONAL MAGNETIC RESONANCE IMAGING; KERNEL METHOD; MACHINE LEARNING; MULTI TASK LEARNING; NEUROIMAGING; PATTERN RECOGNITION; PREDICTIVE VALUE; PRIORITY JOURNAL; REPRODUCIBILITY; ADOLESCENT; ADULT; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; EPISODIC MEMORY; FEMALE; HUMAN; MALE; PHYSIOLOGY; PROCEDURES; SENSITIVITY AND SPECIFICITY; VISUAL CORTEX; YOUNG ADULT;

ADOLESCENT; ADULT; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; BRAIN MAPPING; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; MEMORY, EPISODIC; PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; VISUAL CORTEX; YOUNG ADULT;

EID: 84897801062     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2014.02.008     Document Type: Article

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