New Platform Support and Features in machine learning
framework 1.5
April 27, 2006--In response to increasing user demand, the latest
version of machine learning
framework 1.5 is now available not only for the Windows
platform but for Linux and Mac OS X as well. This Mathematica application
package also adds new features for real-world, multi-method
data-mining projects--including neural networks and quadratic
regression models--and improves existing methods and cross-validation
techniques.
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. With the unprecedented power
of Mathematica's programming environment, machine learning
framework is relied on by industry leaders to customize and
configure machine learning solutions, search for quick modeling
capabilities, and to predict and control input-output
relations. Mathematica's extensive connection technologies make
it easy to integrate machine learning framework into runtime
environments such as process automation and manufacturing management
systems.
In addition to new platform support, machine learning framework
1.5 includes the following new features and enhancements:
- New data manipulation, regression, and classification algorithms,
such as for neural networks and quadratic regression models
- Improved ridge regression and linear regression trees (LIRT)
- Better visualization of linear regression models, as well as LIRT
and ID3 trees
- Increased performance of cross-validation techniques
- Statistics now available for attributes of all major algorithms
- Supplemented error statistics
machine learning framework 1.5 can be downloaded from the
Wolfram web store. Developed and supported by Software Competence
Center Hagenberg (SCCH) GmbH, it requires Mathematica 5.2 or
higher and is now available for Windows, Linux, and Mac OS X. Upgrades
and trial versions are also available.
See the product pages for more information about machine learning
framework.
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