machine learning framework for Mathematica V1.3: Creating interpretable computational models from data

Authors Thomas Natschl├Ąger
Mario Drobics
Felix Kossak
Title machine learning framework for Mathematica V1.3: Creating interpretable computational models from data
Booktitle Proc. Wolfram Technology Conf.
Type in proceedings
Address Champaign, IL, USA
Month October
Year 2005
Pages online
SCCH ID# 531
Abstract

The machine learning framework for Mathematica is a collection of powerful machine learning algorithms integrated into a framework for the 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. The algorithms are highly parameterizeable. Given this parameterizeability combined with the efficient core engine -- realized in C++ -- 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 a large variety of distinct algorithms in an optimized computational kernel and the manipulation, descriptive programming, and graphical capabilities of Mathematica to give the user unforeseen insights into its data.