Source: weka Section: science Priority: optional Maintainer: Debian Java Maintainers Uploaders: Torsten Werner , tony mancill Build-Depends: ant, cup (>=0.11a+20060608), debhelper-compat (= 13), default-jdk, ghostscript, jflex, texlive-latex-base, texlive-latex-extra Standards-Version: 4.5.1 Vcs-Git: https://salsa.debian.org/java-team/weka.git Vcs-Browser: https://salsa.debian.org/java-team/weka Homepage: http://www.cs.waikato.ac.nz/~ml/weka/ 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.