PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The HasGP user manual
Sean Holden
(2011) Technical Report. University of Cambridge, Computer Laboratory, Cambridge, UK.

Abstract

HasGP is an experimental library implementing methods for supervised learning using Gaussian process (GP) inference, in both the regression and classification settings. It has been developed in the functional language Haskell as an investigation into whether the well-known advantages of the functional paradigm can be exploited in the field of machine learning, which traditionally has been dominated by the procedural/object-oriented approach, particularly involving C/C++ and Matlab. HasGP is open-source software released under the GPL3 license. This manual provides a short introduction on how install the library, and how to apply it to supervised learning problems. It also provides some more in-depth information on the implementation of the library, which is aimed at developers. In the latter, we also show how some of the specific functional features of Haskell, in particular the ability to treat functions as first-class objects, and the use of typeclasses and monads, have informed the design of the library. This manual applies to HasGP version 0.1, which is the initial release of the library.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8501
Deposited By:Sean Holden
Deposited On:03 February 2012