SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

BACKGROUND: Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance...

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Detalles Bibliográficos
Autores principales: Sanz, Hector, Valim, Clarissa, Vegas, Esteban, Oller, Josep M., Reverter, Ferran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245920/
https://www.ncbi.nlm.nih.gov/pubmed/30453885
http://dx.doi.org/10.1186/s12859-018-2451-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245920/
https://www.ncbi.nlm.nih.gov/pubmed/30453885
http://dx.doi.org/10.1186/s12859-018-2451-4

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