An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data
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DOI: 10.1016/j.chaos.2020.110055
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References listed on IDEAS
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- Wenhui Ke & Yimin Lu, 2024. "Ensemble Prediction Method Based on Decomposition–Reconstitution–Integration for COVID-19 Outbreak Prediction," Mathematics, MDPI, vol. 12(3), pages 1-20, February.
- Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2023. "Which COVID-19 information really impacts stock markets?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
- James Ming Chen & Mira Zovko & Nika Šimurina & Vatroslav Zovko, 2021. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution," IJERPH, MDPI, vol. 18(16), pages 1-59, August.
- Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
- Gerardo Alfonso Perez & Raquel Castillo, 2023. "Categorical Variable Mapping Considerations in Classification Problems: Protein Application," Mathematics, MDPI, vol. 11(2), pages 1-26, January.
- Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
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Keywords
Bias-variance dilemma; Time series prediction; Support vector machine; Statistical learning; Hyperparameters and chaos; Epidemic spreading;All these keywords.
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