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machine learning framework
The Multimethod System for Creating Understandable Computational Models from Data
machine learning framework is a complete solution for business and financial engineers, process and manufacturing engineers, quality assurance professionals, and all experts who want to extract computational models from data. Knowledge engineers and machine learning experts, who search for a framework to develop customized solutions, will also benefit from its future-looking fuzzy variants of machine learning algorithms in an open architecture.
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Optimized future-looking, fuzzy logic-based machine learning methods and algorithms implemented in C++ are integrated into Mathematica's high-level symbolic computation, visualization, and programming environment. machine learning framework can be a powerful tool for all types of data mining and machine learning applications, including:
* Extraction of features from images and/or signals
* Modeling of chemical, metallurgical, or other continuous processes
* Control of complex discrete manufacturing machines
* Modeling of business and financial systems
The high-end architecture and machine learning approaches are not so different. Select the right methods, and combine them intelligently in a framework with mathematical and knowledge-based technologies. machine learning framework has successfully transferred this combined knowledge in a fast and robust solution for industry leaders.
FEATURES:
The main purpose of machine learning framework is to build abstract models from arbitrary data sources. If an explicit target is identified (supervised learning), the framework can be used to create a model for forecasting this target parameter. If no such target parameter is available (unsupervised learning), the framework can identify related items and create models that classify new items according to this segmentation.
Supervised Analysis
Decision Trees
* FS-ID3 is a fuzzy variant of the ID3 learning algorithm to create decision trees.
Rule Induction
* FS-FOIL is a fuzzy variant of Quinlan's FOIL method.
* FS-MINER is a proprietary method from SCCH GmbH to find cluster descriptions.
Numerical Optimization of Fuzzy Rules
* RENO is a proprietary method from SCCH GmbH that uses numerical optimization to find computationally accurate and robust fuzzy rules.
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 Clustering and Ward Clustering
* Fuzzy c-means clustering creates a fuzzy segmentation of the data.
* Ward clustering is a crisp, agglomerative clustering method.
Tasks
Forecasting
* To apply the created models onto new cases/samples, various inference methods are included.
Classification
* To forecast the membership of a new sample to a previously defined set of classes, decision trees and rule-based methods can be used in a straightforward manner.
Logical Inference
* To predict numerical values using rule bases and decision trees, logical inference methods such as Sugeno and Tagaki-Sugeno-Kang controllers are included. Self-organizing maps (SOMs) are also able to predict new values in a straightforward way.
All of these methods are highly parameterized. The results can be easily visualized using the Mathematica front end. They can also be modified using the Mathematica language to fine-tune the models.
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