weka 3.6.10-2 source package in Ubuntu
Changelog
weka (3.6.10-2) unstable; urgency=low * Add libsvm-java to Suggests and to find_jars in weka launcher. * Update launcher to look for openjdk7 first. * Bump Standards-Version to 3.9.5 (no changes). -- tony mancill <email address hidden> Wed, 04 Dec 2013 21:52:58 -0800
Upload details
- Uploaded by:
- Debian Java Maintainers
- Uploaded to:
- Sid
- Original maintainer:
- Debian Java Maintainers
- Architectures:
- all
- Section:
- science
- Urgency:
- Low Urgency
See full publishing history Publishing
| Series | Published | Component | Section | |
|---|---|---|---|---|
| Trusty | release | universe | science |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| weka_3.6.10-2.dsc | 2.1 KiB | bb7aa0a6d10a595923ad423334741019b8f38500f49a85c0dc01bb8e048df5d1 |
| weka_3.6.10.orig.tar.gz | 13.8 MiB | 538941242f39e23e558a73381eb5dc81cea011a0a32d9ae0f0f3a64a526c704c |
| weka_3.6.10-2.debian.tar.gz | 11.1 KiB | d5f89060c686ffc8fd669b40a5f6ca5378af0fd61f995e3509ed175e2fecbf78 |
Available diffs
- diff from 3.6.10-1 to 3.6.10-2 (1001 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: 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 documentation.
