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• W2182184513 abstract "This article adopts Support Vector Machine (SVM) and Relevance Vector Machine (RVM) for prediction of rainfall in Vellore (India). SVM is firmly based on the theory of statistical learning theory. RVM is a probabilistic basis model. SVM and RVM use air temperature (T), sunshine, humidity and wind speed (V a) as input variables. This article uses SVM and RVM as a regression technique. Equations have been also developed for prediction of rainfall. The developed RVM gives variance of the predicted rainfall. This study shows the RVM is more robust model than the SVM." @default.
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• W2182184513 date "2011-01-01" @default.
• W2182184513 modified "2023-09-29" @default.
• W2182184513 title "Prediction of Rainfall Using Support Vector Machine and Relevance Vector Machine" @default.
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