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

Autoren Mario Drobics
Felix Kossak
Thomas Natschläger
Titelmachine learning framework for Mathematica V1.2 - Creating interpretable computational models from data
BuchtitelProc. Mathematica Technology Conf.
Typin Konferenzband
OrtChampaign, IL, USA
MonatOctober
Jahr2004
Seitenonline
SCCH ID#425
Abstract

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.