Source: weka Priority: optional Maintainer: Debian Java Maintainers Uploaders: Soeren Sonnenburg , Torsten Werner , tony mancill Build-Depends: cdbs, debhelper (>= 9), default-jdk, ant, texlive-latex-base, texlive-latex-extra, ghostscript, jflex, cup (>=0.11a+20060608) Standards-Version: 3.9.8 Section: science Homepage: http://www.cs.waikato.ac.nz/~ml/weka/ Vcs-Git: https://anonscm.debian.org/git/pkg-java/weka.git Vcs-Browser: https://anonscm.debian.org/cgit/pkg-java/weka.git Package: weka Architecture: all Depends: ${shlibs:Depends}, ${misc:Depends}, default-jre | java7-runtime | java6-runtime, java-wrappers, cup (>=0.11a+20060608) 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.