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Source: weka
Section: science
Priority: optional
Maintainer: Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org>
Uploaders:
Torsten Werner <twerner@debian.org>,
tony mancill <tmancill@debian.org>
Build-Depends:
ant,
cup (>=0.11a+20060608),
debhelper-compat (= 13),
default-jdk,
ghostscript,
jflex,
texlive-latex-base,
texlive-latex-extra
Standards-Version: 4.6.2
Vcs-Git: https://salsa.debian.org/java-team/weka.git
Vcs-Browser: https://salsa.debian.org/java-team/weka
Homepage: https://www.cs.waikato.ac.nz/~ml/weka/
Rules-Requires-Root: no
Package: weka
Architecture: all
Depends:
cup (>=0.11a+20060608),
default-jre | java7-runtime | java6-runtime,
java-wrappers,
${misc:Depends},
${shlibs:Depends}
Suggests: libsvm-java
Description: 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 binaries and examples.
Package: weka-doc
Architecture: all
Depends: ${misc:Depends}
Recommends: weka
Section: doc
Description: documentation for the Weka machine learning suite
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.
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