TR 2008/224, FIT, Monash University,
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
classification- (decision-) trees and other regressions, and
mixed Bayesian networks,
provided only that the types match between each corresponding
structured model, and
discrete, continuous, sequence, multivariate, and so on.
- [Paper.ps], [Paper.pdf].
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