Machine Learning Framework creating understandable computational models with Mathematica
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Machine Learning Framework Tags

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Machine Learning Framework Description

The machine learning framework for Mathematica is a collection of powerful machine learning algorithms integrated into a framework for the main purpose of data analysis. Fuzzy logic is one of its key techniques. The framework allows for combining different machine learning algorithms to solve one single problem. This combination of distinct algorithms may give the user unforeseen insights into its data. The algorithms are highly parameterizable. Given this parameterizability combined with the efficient core engine of the machine learning framework for Mathematica, the user is able to analyze their data interactively, with short cycles of changing parameter settings and examining the results. The machine learning framework for Mathematica covers a wide range of machine learning algorithms which can be integrated to work together and therefore yield new results. What's new in this version: fuzzy decision trees fuzzy rule learning fuzzy regression trees cluster descriptions optimization of fuzzy controllers self-organizing maps automated model testing advanced data visualization See release notes

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