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)

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  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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