RSS Feed - Blog
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
|
Projects authored by bernhard pfahringer.
Search
Manage
Sort by
Filter by
Latest Thoughts
- MLOSS workshop at ICML 2015: Open Ecosystems
by Antti Honkela on March 30, 2015
- A third of the top 100 papers are about software
by Cheng Soon Ong on October 30, 2014
- Open Machine Learning Workshop
by Cheng Soon Ong on July 28, 2014
- Machine Learning Distro
by Cheng Soon Ong on July 22, 2014
- Google Summer of Code 2014
by Cheng Soon Ong on June 3, 2014
- Reproducibility is not simple
by Cheng Soon Ong on March 30, 2014
- Keynotes at ACML 2013
by Cheng Soon Ong on November 14, 2013
- What does the “OSS” in MLOSS mean?
by Mark Reid on September 1, 2013
- Code review for science
by Cheng Soon Ong on August 14, 2013
- GSoC 2013
by Cheng Soon Ong on April 9, 2013
No forum yet.
