In this paper, we evaluate the gaussian process (GP) as a powerful toolkit for nonparametric classification, and regression. Unlike traditional parametric methods, GPs provide a distribution over functional spaces to model the uncertainty in predictions. The relationship between GP and input correlation kernel functions are illustrated, and some different kernels are introduced. Moreover, practical applications of GP for large scale problems using the Nyström approximation have been studied, and several numerical examples have been provided to verify the validity and efficiency of the proposed method. The implementation codes have been executed in Python using Scikit-learn library.
Zaferanieh,M. , Shafiee Fard,A. , jafarzadeh,M. and Hasanpoor,H. (2025). Restricted gaussian process for predicting latent functions. Journal of Frame and Matrix Theory, 2(2), 56-74. doi: 10.22034/jfmt.2025.504073.1024
MLA
Zaferanieh,M. , , Shafiee Fard,A. , , jafarzadeh,M. , and Hasanpoor,H. . "Restricted gaussian process for predicting latent functions", Journal of Frame and Matrix Theory, 2, 2, 2025, 56-74. doi: 10.22034/jfmt.2025.504073.1024
HARVARD
Zaferanieh M., Shafiee Fard A., jafarzadeh M., Hasanpoor H. (2025). 'Restricted gaussian process for predicting latent functions', Journal of Frame and Matrix Theory, 2(2), pp. 56-74. doi: 10.22034/jfmt.2025.504073.1024
CHICAGO
M. Zaferanieh, A. Shafiee Fard, M. jafarzadeh and H. Hasanpoor, "Restricted gaussian process for predicting latent functions," Journal of Frame and Matrix Theory, 2 2 (2025): 56-74, doi: 10.22034/jfmt.2025.504073.1024
VANCOUVER
Zaferanieh M., Shafiee Fard A., jafarzadeh M., Hasanpoor H. Restricted gaussian process for predicting latent functions. JFMT, 2025; 2(2): 56-74. doi: 10.22034/jfmt.2025.504073.1024
