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An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable
Supervised machine learning methods typically require splitting data into multiple chunks for training, validating, and finally testing classifiers. For finding the best parameters of a classifier, training and validation are usually carried out with cross-validation. This is followed by application...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2016
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001642/ https://www.ncbi.nlm.nih.gov/pubmed/27564393 http://dx.doi.org/10.1371/journal.pone.0161788 |