PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

HasGP: A Haskell Library for Gaussian Process Inference
Sean Holden
. Journal of Machine Learning Research, 2011.

Abstract

HasGP is a library providing supervised learning algorithms for Gaussian process (GP) regression and classification. While only one of many GP libraries available, it differs in that it represents an ongoing exploration of how machine learning research and deployment might benefit by moving away from the imperative/object-oriented style of implementation and instead employing the functional programming (FP) paradigm. HasGP is implemented in Haskell and is available under the GPL3 open source license.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:8502
Deposited By:Sean Holden
Deposited On:03 February 2012