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...

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Detalles Bibliográficos
Autores principales: Peng, Yaohao, Nagata, Mateus Hiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Ltd. 2020
Materias:
Article
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

Internet

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

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