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...

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Detalles Bibliográficos
Autores principales: Monteiro, João M., Rao, Anil, Shawe-Taylor, John, Mourão-Miranda, Janaina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier/North-Holland Biomedical Press 2016
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012894/
https://www.ncbi.nlm.nih.gov/pubmed/27353722
http://dx.doi.org/10.1016/j.jneumeth.2016.06.011

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5012894/
https://www.ncbi.nlm.nih.gov/pubmed/27353722
http://dx.doi.org/10.1016/j.jneumeth.2016.06.011

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