Machine Learning
Machine Learning
The justification of logical theories based on data compression
Machine intelligence 13
Domain-specific languages: an annotated bibliography
ACM SIGPLAN Notices
Template meta-programming for Haskell
Proceedings of the 2002 ACM SIGPLAN workshop on Haskell
Types and classes of machine learning and data mining
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Models for machine learning and data mining in functional programming
Journal of Functional Programming
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Modelling-Alignment for non-random sequences
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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Inductive programming is a new machine learning paradigm which combines functional programming for writing statistical models and information theory to prevent overfitting, Type-classes specify general properties that models must have. Many statistical models, estimators and operators have polymorphic types. Useful operators combine models, and estimators, to form new ones; Functional programmings's compositional style of programming is a great advantage in this domain, Complementing this, information theory provides a compositional measure of the complexity of a model from its parts.Inductive programming is illustrated by a case study of Bayesian networks, Networks are built from classification- (decision-) trees. Trees are built from partioning functions and models on data-spaces. Trees, and hence networks, are general as a natural consequence of the method. Discrete and continious variables, and missing values are handled by the networks. Finally the Bayesian networks are applied to a challenging data set on lost persons.