Abstract
We describe a new data-mining platform, CDMS, aimed at the streamlined development, comparison and application of machine learning tools. We discuss its type system, focussing on the treatment of statistical models as first-class values.
This allows rapid construction of composite models – complex models built from simpler ones – such as mixture models, Bayesian networks and decision trees. We illustrate this with a flexible decision tree tool for CDMS which rather than being limited to discrete target attributes, can model any kind of data using arbitrary probability distributions.
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Comley, J.W., Allison, L., Fitzgibbon, L.J. (2003). Flexible Decision Trees in a General Data-Mining Environment. In: Liu, J., Cheung, Ym., Yin, H. (eds) Intelligent Data Engineering and Automated Learning. IDEAL 2003. Lecture Notes in Computer Science, vol 2690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45080-1_102
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DOI: https://doi.org/10.1007/978-3-540-45080-1_102
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