A framework for learning from a continuous supply of examples, a data stream. Includes classification and clustering methods. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
ADAMS is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes.
Instead of placing operators on a canvas and manually connecting them, a tree structure and flow control operators determine how data is
... [More] processed (sequentially/parallel). This allows rapid development and easy maintenance of large workflows, with hundreds or thousands of operators.
Operators include machine learning (WEKA, MOA, MEKA) and image processing (ImageJ, JAI, BoofCV, OpenImaJ, LIRE, ImageMagick and Gnuplot). R available using Rserve. WEKA webservice allows other frameworks to use WEKA models. Fast prototyping with Groovy and Jython. Read/write support for various databases and spreadsheet applications. [Less]
KeplerWeka adds the functionality of the open-source machine learning and data mining workbench WEKA to the free and open-source, scientific workflow application, Kepler.
Code behind the keywords4bytecodes.org project.
Machine learning on 330k de-compiled Java methods from the Debian archive plus their corresponding Java Doc textual comments. Input are bytecodes, output are English keywords.
A collection of plug-in algorithms for the WEKA machine learning workbench including artificial neural network (ANN) algorithms, and artificial immune system (AIS) algorithms.
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