A comparative study on the performance of covariance functions in gaussian process regression model : Application to global wheat price

• Main Authors: Nahamizun, Maamor, Hanita, Daud, Muhammad Naeim, Mohd Aris, Nor Izzati, Jaini, Mahmod, Othman, Evizal, Abdul Kadir
• Format: Article
• Language: English
• Published: Semarak Ilmu Publishing 2024
• Subjects: HD Industries. Land use. Labor; Q Science (General); QA Mathematics; T Technology (General)
• Online Access: http://umpir.ump.edu.my/id/eprint/41357/1/A%20comparative%20study%20on%20the%20performance%20of%20covariance%20functions%20in%20gaussian.pdf; http://umpir.ump.edu.my/id/eprint/41357/; https://doi.org/10.37934/araset.42.1.215225; https://doi.org/10.37934/araset.42.1.215225

Gaussian Process Regression (GPR) is a nonparametric machine learning model that provides uncertainty quantification in making predictions. GPR utilizes several covariance functions (CFs) in the process of developing models to ensure high accuracy. There are five common CFs in GPR, which are the Rad...