A Programming Paradigm for Machine Learning, with a Case Study of Bayesian Networks.

Lloyd Allison,
ACSC2006, pp.103-111, January 2006.

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Abstract: Inductive programming is a new machine learning paradigm which combines functional programming for writing statistical models and information theory to prevent overfitting. Type-classes specify general properties that models must have. Many statistical models, estimators and operators have polymorphic types. Useful operators combine models, and estimators, to form new ones; Functional programming's compositional style of programming is a great advantage in this domain. Complementing this, information theory provides a compositional measure of the complexity of a model from its parts.

Inductive programming is illustrated by a case study of Bayesian networks. Networks are built from classification- (decision-) trees. Trees are built from partitioning functions and models on data-spaces. Trees, and hence networks, are general as a natural consequence of the method. Discrete and continuous variables, and missing values are handled by the networks. Finally the Bayesian networks are applied to a challenging data set on lost persons.

Keywords:  inductive inference, functional programming, Haskell, minimum length encoding, statistical models, Bayesian networks.

generalized mixed Bayesian Network for Missing Persons inferred from Search and Rescue Data, ACSC2006, 29th Australasian Computer Science Conference
Bayesian Network for Missing Persons inferred from behaviour recorded in Search and Rescue Data.
  

[Paper.pdf], [Paper.ps] or [Paper.pdf]['06].

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© L. Allison   http://www.allisons.org/ll/   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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