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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...
में बचाया:
| में प्रकाशित: | J Neurosci Methods |
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| मुख्य लेखकों: | , , , |
| स्वरूप: | Artigo |
| भाषा: | Inglês |
| प्रकाशित: |
Elsevier/North-Holland Biomedical Press
2016
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| विषय: | |
| ऑनलाइन पहुंच: | 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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