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Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets.
This package contains the documentation.
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weka (3.6.6-1) unstable; urgency=low * Team upload. * New upstream version (Closes: #649734) * Update rules and build-deps for default-jdk and default-jre. -- tony mancill <firstname.lastname@example.org> Thu, 24 Nov 2011 12:35:17 -0800 weka (3.6.5-1) unstable; urgency=low * Team upload. * New upstream version (Closes: #632082, #598400) * Bump Standards-Version to 3.9.2 (no changes required more»
This package was debianized by Soeren Sonnenburg <email@example.com>. It was downloaded from http://www.cs.waikato.ac.nz/ml/weka/ . The upstream Author is the Waikato Machine Learning Group <firstname.lastname@example.org>, cf. http://www.cs.waikato.ac.nz/~ml/people.html. If not otherwise mentioned Copyright: (C) 1998-2008 University of Waikato. License: This program is free software; y more»
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