Gaussian Process Algorithm

(Redirected from Gaussian Processes)
Jump to: navigation, search

A Gaussian Process Algorithm is kernel-based algorithm/nonparametric statistical modeling algorithm that fits a Gaussian Process model.



    • QUOTE: An advantage of Gaussian Processes is that, like other kernel methods, they can be optimized exactly, given the values of their hyper-parameters (such as the weight decay and the spread of a Gaussian kernel), and this often allows a fine and precise trade-off between fitting the data and smoothing. On small datasets they are very good because of this well-tuned smoothing and because they are still computationally affordable. They are my method of choice for small regression datasets (less than 1000 or 2000 examples). On the other hand, if you want to capture a complicated function (with many many ups and downs, i.e., not necessarily very smooth), then you need a model that can scale to large datasets and that can generalize non-locally (which kernel machines with standard generic kernels, typically local, do not provide). Modern variants of neural networks (so-called Deep Learning, Deep Learning) are more attractive with respect to these two properties, so I would prefer them for larger datasets where there is a lot of structure to be extracted from the data (the target function is not smooth).