Added Distributions for use in Clustering (Mixture Modelling), Function Models, Regression Trees, Segmentation, and mixed Bayesian Networks in Inductive Programming 1.2 (IP 1.2)

and the

  IP 1.2

Lloyd Allison,
TR 2008/224, FIT, Monash University,
April 2008

Inductive programming is a machine learning paradigm combining functional programming (FP) with the information theoretic criterion, Minimum Message Length (MML). IP 1.2 now includes the Geometric and Poisson distributions over non-negative integers, and Student's t-Distribution over continuous values, as well as the Multinomial and Normal (Gaussian) distributions from before. All of these can be used with IP's model-transformation operators, and structure-learning algorithms including clustering (mixture-models), classification- (decision-) trees and other regressions, and mixed Bayesian networks, provided only that the types match between each corresponding component Model, transformation, structured model, and variable -- discrete, continuous, sequence, multivariate, and so on.

[], [Paper.pdf].
Coding Ockham's Razor, L. Allison, Springer

A Practical Introduction to Denotational Semantics, L. Allison, CUP

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© L. Allison   (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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