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weka-doc

Machine learning algorithms for data mining tasks

Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes.

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.

Homepage:
Package version:3.6.6-1
Architecture:all
Distribution:Ubuntu
Filename:weka-doc_3.6.6-1_all.deb

/usr/share/doc/weka-doc/README.gz

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/usr/share/doc/weka-doc/changelog.Debian.gz

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 <tmancill@debian.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
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/usr/share/doc/weka-doc/copyright

This package was debianized by Soeren Sonnenburg <sonne@debian.org>.

It was downloaded from http://www.cs.waikato.ac.nz/ml/weka/ .

The upstream Author is the Waikato Machine Learning Group
<wekalist@list.scms.waikato.ac.nz>, 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
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/usr/share/doc/weka-doc/doc/weka/core/Copyright.html

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