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• W3202022212 abstract "In this paper, a nonparametric kernel prediction algorithm in machine learning is applied to predict CO 2 emissions. A literature review has been conducted so that proper independent variables can be identified. Traditional parametric modeling approaches and the Gaussian Process Regression (GPR) algorithms were introduced, and their prediction performance was summarized. The reliability and efficiency of the proposed algorithms were then demonstrated through the comparison of the actual and the predicted results. The results showed that the GPR method can give the most accurate predictions on CO 2 emissions." @default.
• W3202022212 created "2021-10-11" @default.
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• W3202022212 date "2021-09-24" @default.
• W3202022212 modified "2023-10-15" @default.
• W3202022212 title "Can Machine Learning be Applied to Carbon Emissions Analysis: An Application to the CO2 Emissions Analysis Using Gaussian Process Regression" @default.
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• W3202022212 doi "https://doi.org/10.3389/fenrg.2021.756311" @default.
• W3202022212 hasPublicationYear "2021" @default.
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