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Generative Embedding for Model-Based Classification of fMRI Data Kay H. Brodersen1,2*, Thomas M. Schofield3, Alexander P. Leff3, Cheng Soon Ong1, Ekaterina I. Lomakina1,2, Joachim M. Buhmann1, Klaas E. Stephan2,3 1 Depar
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Document Date: 2016-06-09 08:04:48

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City

Zurich / London / /

Company

APL / Creative Commons / Wellcome Trust / Siemens / /

Country

Switzerland / /

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Facility

Montreal Neurological Institute / University Research Priority Program / Indiana University / University College London / University College Hospitals London / University of Zurich / /

IndustryTerm

classification algorithm / multivariate classification algorithms / inner product / sparse solution / designed algorithms / functional magnetic resonance imaging / treatment of such disorders / clinical tools / thalamo-temporal network / dense solution / speech processing / classification algorithms / line search / difference-of-convex-functions algorithm / learning algorithm / variational free-energy / learning algorithms / /

Organization

Department of Economics / Wellcome Trust Centre for Neuroimaging / Department of Computer Science / US Federal Reserve / Laboratory for Social and Neural Systems Research / University College London / London / Montreal Neurological Institute / University of Zurich / Zurich / Indiana University / /

Person

Cheng Soon / Az / /

Position

Author / original author / Editor / model for the test subject / speaker / Fisher / representative / /

ProgrammingLanguage

C / /

PublishedMedium

PLoS Computational Biology / /

Technology

difference-of-convex-functions algorithm / learning algorithm / broadband / magnetic resonance imaging / tomography / classification algorithms / carefully designed algorithms / classification algorithm / MRI / underlying multivariate classification algorithms / /

URL

http /

SocialTag