weka 3.6.14-1 source package in Ubuntu

Changelog

weka (3.6.14-1) unstable; urgency=medium

  * New upstream version.
  * Tweak weka-doc package description.
  * Bump Standards-Version to 3.9.8 (no changes).
  * Use HTTPS for Vcs URLs.

 -- tony mancill <email address hidden>  Sun, 07 Aug 2016 19:29:59 -0700

Upload details

Uploaded by:
Debian Java Maintainers on 2016-08-08
Uploaded to:
Sid
Original maintainer:
Debian Java Maintainers
Architectures:
all
Section:
science
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Focal release on 2019-10-18 universe science
Eoan release on 2019-04-18 universe science
Disco release on 2018-10-30 universe science
Cosmic release on 2018-05-01 universe science
Bionic release on 2017-10-24 universe science
Artful release on 2017-04-20 universe science

Builds

Yakkety: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
weka_3.6.14-1.dsc 2.1 KiB 3c5fc87c2997086adbc0ad7cbce487dd6338f20ee26d2e116bf20b74a6605258
weka_3.6.14.orig.tar.gz 13.9 MiB bef592188ef4da3488c6043e782c6c8cea42877364d8a2be68d4d61b9a602368
weka_3.6.14-1.debian.tar.xz 10.2 KiB a1c31c26cc668dfb87e746b3c06f980e741d9258e63a43f8e78be4fe92503d92

Available diffs

No changes file available.

Binary packages built by this source

weka: 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.

weka-doc: 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.