Machine Learning Framework Machine learning framework - A collection of powerful machine learning algorithms integrated into a framework
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Machine learning framework - A collection of powerful machine learning algorithms integrated into a framework 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 makes available a large number of machine learning algorithms that may be computed to work together and therefore yield new results.Here are some key features of "machine learning framework":Supervised Analysis· uzzy decision trees: FS-ID3, a fuzzy variant of the ID3 learning algorithm to create decision trees· fuzzy rule generation: FS-FOIL, a fuzzy variant of Quinlan's FOIL method· cluster descriptions: FS-MINER, a proprietary method to find cluster descriptions· optimization of fuzzy controllers: RENO, a proprietary method, which uses numerical optimization to find computationally accurate and robust fuzzy rules· Ridge Regression: Regression with built-in feature selection. Unsupervised Analysis· Self-organizing maps: create two-dimensional plots of high dimensional data sets, preprocess large and noisy data sets, recall (one or more) missing values in the data· fuzzy c-means: creates a fuzzy segmentation of the data· Ward clustering: a crisp, agglomerative clustering method Additional features· Powerful functions for routine tasks· Automated model testing· ODBC Data import· Advanced data visualization· Fuzzy logic (using different types of fuzzy sets and t-norms)· Fuzzy inference (Mamdani, Sugeno, Tagaki-Sugeno-Kang)Requirements:· Mathematica 5.2 (or later). What's New in This Release: · mlf was adapted for Mathematica 6.0 · A major memory leak in the mlf kernel was plugged, considerably enhancing performance in certain cases.

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