machine learning framework for Mathematica V1.2 - Creating interpretable computational models from data

Author(s)	Mario Drobics Felix Kossak Thomas Natschläger
Title	machine learning framework for Mathematica V1.2 - Creating interpretable computational models from data
Booktitle	Proc. Mathematica Technology Conf.
Typ	Inproceedings
Month	October
Year	2004
Pages	online
Address	Champaign, IL, USA
SCCH #	0425
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 parameterizeable.Given this parameterizeability combined with the efficient core engine of the machine learning framework for Mathematica,the user is able to look at his data with changed parameters in real time.The machine learning framework for Mathematica combines -an optimized computational kernel—the core engine—realized in C++ -and the manipulation,descriptive programming,and graphical capabilities of Mathematica.