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An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data
In this paper, we applied support vector regression to predict the number of COVID-19 cases for the 12 most-affected countries, testing for different structures of nonlinearity using Kernel functions and analyzing the sensitivity of the models’ predictive performance to different hyperparameters set...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier Ltd.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7324351/ https://www.ncbi.nlm.nih.gov/pubmed/32834608 http://dx.doi.org/10.1016/j.chaos.2020.110055 |