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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:3.6.0-2
Architecture:all
Distribution:Ubuntu
Filename:weka-doc_3.6.0-2_all.deb

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

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

weka (3.6.0-2) unstable; urgency=low

  * Change Maintainer: Debian Java Maintainers.
  * Update Standards-Version: 3.8.2 (no changes).

 -- Torsten Werner <twerner@debian.org>  Mon, 29 Jun 2009 22:02:28 +0200

weka (3.6.0-1) unstable; urgency=low

  * New (stable) upstream version.
  * Update copyright and patches to reflect upstream changes.
  * Build single .pdf document.
  * Add ${misc:Depends
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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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