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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Kholod, Ivan Kupriyanov, Mikhail and Shorov, Andrey 2016. Decomposition of Data Mining Algorithms into Unified Functional Blocks. Mathematical Problems in Engineering, Vol. 2016, p. 1.

    Ng, K.S. and Lloyd, J.W. 2009. Probabilistic reasoning in a classical logic. Journal of Applied Logic, Vol. 7, Issue. 2, p. 218.

    Dowe, D. L. 2008. Foreword re C. S. Wallace. The Computer Journal, Vol. 51, Issue. 5, p. 523.

    Ng, K. S. Lloyd, J. W. and Uther, W. T. B. 2008. Probabilistic modelling, inference and learning using logical theories. Annals of Mathematics and Artificial Intelligence, Vol. 54, Issue. 1-3, p. 159.

    Dale, Michael B. Allison, Lloyd and Dale, Patricia E.R. 2007. Segmentation and clustering as complementary sources of information. Acta Oecologica, Vol. 31, Issue. 2, p. 193.


Models for machine learning and data mining in functional programming

  • DOI:
  • Published online: 01 January 2005

The functional programming language Haskell and its type system are used to define and analyse the nature of some problems and tools in machine learning and data mining. Data types and type-classes for statistical models are developed that allow models to be manipulated in a precise, type-safe and flexible way. The statistical models considered include probability distributions, mixture models, function-models, time-series, and classification- and function-model-trees. The aim is to improve ways of designing and programming with models, not only of applying them.

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Journal of Functional Programming
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  • URL: /core/journals/journal-of-functional-programming
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