HasGP: A Haskell library for inference using Gaussian processes
A Haskell library implementing algorithms for supervised learning, roughly corresponding to chapters 1 to 5 of "Gaussian Processes for Machine Learning" by Carl Rasmussen and Christopher Williams, The MIT Press 2006. In particular, algorithms are provides for regression and for two-class classification using either the Laplace or EP approximation.
[Skip to Readme]
Versions [faq] | 0.1 |
---|---|
Dependencies | base (==4.*), haskell98 (==1.*), hmatrix (==0.12.*), hmatrix-special (==0.1.*), mtl (==2.*), parsec (==3.*), random (==1.*) [details] |
License | GPL-3.0-only |
Copyright | Copyright (C) 2011 Sean Holden |
Author | Sean B. Holden |
Maintainer | sbh11@cl.cam.ac.uk |
Category | AI, Classification, Datamining, Statistics |
Home page | http://www.cl.cam.ac.uk/~sbh11/HasGP |
Bug tracker | sbh11@cl.cam.ac.uk |
Uploaded | by SeanHolden at Wed Oct 26 15:35:53 UTC 2011 |
Distributions | NixOS:0.1 |
Downloads | 773 total (16 in the last 30 days) |
Rating | (no votes yet) [estimated by rule of succession] |
Your Rating | |
Status | Docs uploaded by user Build status unknown [no reports yet] |
Downloads
- HasGP-0.1.tar.gz [browse] (Cabal source package)
- Package description (as included in the package)