HasGP: A Haskell library for inference using Gaussian processes

[ ai, classification, datamining, gpl, library, statistics ] [ Propose Tags ]

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.

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Versions [RSS] [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 2011-10-26T15:35:53Z
Distributions NixOS:0.1
Downloads 1278 total (7 in the last 30 days)
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Readme for HasGP-0.1

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The HasGP package for Gaussian process inference in Haskell

Copyright (C) 2011 Sean Holden sbh11@cl.cam.ac.uk

For a detailed description of how to install, use and modify this code 
please download the User Manual from the project site at: