Added Distributions for use in Clustering (Mixture Modelling), Function Models, Regression Trees, Segmentation, and mixed Bayesian Networks in Inductive Programming 1.2
- Lloyd Allison,
- TR 2008/224, FIT, Monash University,
- April 2008
- 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 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], [source-code].