ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Gruber, Peter, Meyer-Bäse, Anke, Foo, Simon and Theis, Fabian J. (2009) ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data. Engineering Applications of Artificial Intelligence 22 (4-5), p. 1111.Date of publication of this fulltext: 01 Oct 2010 08:04
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| Item type | Article | ||||
| Journal or Publication Title | Engineering Applications of Artificial Intelligence | ||||
| Publisher: | Pergamon Press | ||||
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| Volume: | 22 | ||||
| Number of Issue or Book Chapter: | 4-5 | ||||
| Page Range: | p. 1111 | ||||
| Date | 2009 | ||||
| Institutions | Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang | ||||
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| Keywords | Genetic Algorithm; Kernel Method; ROC; Nonnegative matrix factorization; Sparseness; PCA | ||||
| Dewey Decimal Classification | 500 Science > 570 Life sciences | ||||
| Status | Published | ||||
| Refereed | Unknown | ||||
| Created at the University of Regensburg | Unknown | ||||
| Item ID | 16865 |
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