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

Autoren Thomas Natschläger
Mario Drobics
Felix Kossak
Titelmachine learning framework for Mathematica V1.3: Creating interpretable computational models from data
BuchtitelProc. Wolfram Technology Conf.
Typin Konferenzband
OrtChampaign, IL, USA
MonatOctober
Jahr2005
Seitenonline
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.