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Added Distributions for use in Clustering (Mixture Modelling), Function Models,

Summary: Added Distributions for use in Clustering
(Mixture Modelling), Function Models,
Regression Trees, Segmentation, and mixed
Bayesian Networks in Inductive
Programming 1.2.
Lloyd Allison
Faculty of Information Technology,
Monash University, Clayton, Victoria, Australia 3800.
April 2008.
TR 2008/224
Abstract. Inductive programming is a machine learning paradigm com-
bining functional programming (FP) with the information theoretic crite-
rion, Minimum Message Length (MML). IP 1.2 now includes the Geomet-
ric 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 algo-
rithms including clustering (mixture-models), classi cation- (decision-)
trees and other regressions, and mixed Bayesian networks, provided only
that the types match between each corresponding component Model,


Source: Allison, Lloyd - Caulfield School of Information Technology, Monash University


Collections: Computer Technologies and Information Sciences