C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Alan Turing: The Enigma
LISP 1.5 Programmer's Manual
A programming language
A Mathematical Theory of Communication
A Mathematical Theory of Communication
A programming paradigm for machine learning, with a case study of Bayesian networks
ACSC '06 Proceedings of the 29th Australasian Computer Science Conference - Volume 48
Declarative programming for artificial intelligence applications
ICFP '07 Proceedings of the 12th ACM SIGPLAN international conference on Functional programming
Probabilistic modelling, inference and learning using logical theories
Annals of Mathematics and Artificial Intelligence
Towards a general framework for data mining
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
Learning hybrid bayesian networks by MML
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The functional programming language Haskell and its type system are used to define and analyse the nature of some problems and tools in machine learning and data mining. Data types and type-classes for statistical models are developed that allow models to be manipulated in a precise, type-safe and flexible way. The statistical models considered include probability distributions, mixture models, function-models, time-series, and classification- and function-model-trees. The aim is to improve ways of designing and programming with models, not only of applying them.