weka 3.6.14-3 source package in Ubuntu
Changelog
weka (3.6.14-3) unstable; urgency=medium
* Team Upload.
* Add d/salsa-ci.yml
* d/p/reproducible.patch: Remove date from doc to
make build reproducible (Closes: #986642)
* Fix pdf docbase name
-- Nilesh Patra <email address hidden> Sun, 15 Aug 2021 16:44:20 +0530
Upload details
- Uploaded by:
- Debian Java Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian Java Maintainers
- Architectures:
- all
- Section:
- science
- Urgency:
- Medium Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Mantic | release | universe | science | |
| Lunar | release | universe | science | |
| Jammy | release | universe | science |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| weka_3.6.14-3.dsc | 2.1 KiB | c8c45cb6f6feae8b11cd4769395ef9fa7f29979202f65078f33b9d2bd4b5ac26 |
| weka_3.6.14.orig.tar.gz | 13.9 MiB | bef592188ef4da3488c6043e782c6c8cea42877364d8a2be68d4d61b9a602368 |
| weka_3.6.14-3.debian.tar.xz | 10.5 KiB | 2116f02ba2015abb08d1658c9f6e649ddaae8d0473425692211713c0834c8bee |
Available diffs
- diff from 3.6.14-2 to 3.6.14-3 (781 bytes)
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.
