There are roughly a dozen major regression techniques, and each technique has several variations. The most common techniques include linear regression, linear ridge regression, k-nearest neighbors ...

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 ...

Gaussian process regression is a sophisticated technique that uses what is called the kernel trick to deal with complex non-linear data, and L2 regularization to avoid model overfitting where a model ...