Projects that are tagged with weka.


Logo AutoWEKA 2.0

by larsko - May 19, 2016, 19:58:41 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 300 views, 44 downloads, 1 subscription

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.

Changes:

Initial Announcement on mloss.org.


Logo python weka wrapper 0.3.8

by fracpete - May 9, 2016, 04:17:42 CET [ Project Homepage BibTeX Download ] 27627 views, 5621 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • works now with javabridge 1.0.14

Logo ADAMS 0.4.12

by fracpete - December 21, 2015, 22:48:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19807 views, 3887 downloads, 3 subscriptions

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows.

Changes:

Some highlights of this release:

  • added adams-nlp package for some basic natural language processing (Stanford parser, tweet parsing)
  • VLC-based video player
  • Fonts can be customized now via preferences dialog (e.g. for better unicode support)
  • Flows can be saved/loaded with custom encodings
  • Many tweaks to search, preview browser, flow editor to improve interaction

Logo PyScriptClassifier 0.3.0

by cjb60 - November 25, 2015, 04:07:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2119 views, 530 downloads, 2 subscriptions

About: Easily prototype WEKA classifiers and filters using Python scripts.

Changes:

0.3.0

  • Filters have now been implemented.
  • Classifier and filter classes satisfy base unit tests.

0.2.1

  • Can now choose to save the script in the model using the -save flag.

0.2.0

  • Added Python 3 support.
  • Added uses decorator to prevent non-essential arguments from being passed.
  • Fixed nasty bug where imputation, binarisation, and standardisation would not actually be applied to test instances.
  • GUI in WEKA now displays the exception as well.
  • Fixed bug where single quotes in attribute values could mess up args creation.
  • ArffToPickle now recognises class index option and arguments.
  • Fix nasty bug where filters were not being saved and were made from scratch from test data.

0.1.1

  • ArffToArgs gets temporary folder in a platform-independent way, instead of assuming /tmp/.
  • Can now save args in ArffToPickle using save.

0.1.0

  • Initial release.

Logo JMLR MOA Massive Online Analysis Nov-13

by abifet - April 4, 2014, 03:50:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16926 views, 6170 downloads, 1 subscription

About: Massive Online Analysis (MOA) is a real time analytic tool for data streams. It is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naive Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and it is released under the GNU GPL license.

Changes:

New version November 2013


Logo mldata-utils 0.5.0

by sonne - April 8, 2011, 10:02:44 CET [ Project Homepage BibTeX Download ] 28486 views, 6204 downloads, 1 subscription

About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org

Changes:
  • Change task file format, such that data splits can have a variable number items and put into up to 256 categories of training/validation/test/not used/...
  • Various bugfixes.

Logo pHMM4weka 1.0

by smm52 - October 22, 2010, 03:48:07 CET [ Project Homepage BibTeX Download ] 4667 views, 1374 downloads, 1 subscription

About: This Java software implements Profile Hidden Markov Models (PHMMs) for protein classification for the WEKA workbench. Standard PHMMs and newly introduced binary PHMMs are used. In addition the software allows propositionalisation of PHMMs.

Changes:

description changed