Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even ...

As mentioned earlier, GPR can handle categorical predictor variables by using one-hot encoding. A regression technique that is closely related to Gaussian process regression is kernel ridge regression ...

The Data Science Lab Gaussian Process Regression from Scratch Using C# GPR works well with small datasets and generates a metric of confidence of a predicted result, but it's moderately complex and ...

Poisson, or other non-Gaussian distribution from an exponential family, while the covariates are mixed functional and scalar variables. The proposed model offers a nonparametric generalized concurrent ...

The model based on Gaussian process (GP) prior and a kernel covariance function can be used to fit nonlinear data with multidimensional covariates. It has been used as a flexible nonparametric ...