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A multiple hold-out framework for Sparse Partial Least Squares
BACKGROUND: Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's...
Αποθηκεύτηκε σε:
| Τόπος έκδοσης: | J Neurosci Methods |
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| Κύριοι συγγραφείς: | , , , |
| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
Elsevier/North-Holland Biomedical Press
2016
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| Θέματα: | |
| Διαθέσιμο Online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5012894/ https://ncbi.nlm.nih.gov/pubmed/27353722 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jneumeth.2016.06.011 |
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