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- Lloyd Allison,
- TR 2008/224, FIT, Monash University,
- April 2008
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- 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.
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- [Paper.ps],
[Paper.pdf],
[source-code].
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