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Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM
Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiti...
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| Опубликовано в: : | Front Hum Neurosci |
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| Главные авторы: | , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Frontiers Media S.A.
2015
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4424860/ https://ncbi.nlm.nih.gov/pubmed/26005413 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnhum.2015.00259 |
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