Weka, MOA and Experiment Databases: Frameworks for Machine Learning

author: Geoffrey Holmes, Faculty of Computing and Mathematical Sciences, University of Waikato
published: March 27, 2014,   recorded: November 2013,   views: 3354
Categories

Slides

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

This talk is in three parts. The first deals with an aspect of the Weka project that has received little attention, namely the use of machine learning in agricultural applications. I will outline our experiences in this field and present an application development framework which is a direct result of this activity. In particular, one project has met one of the challenges proposed by Kiri Wagstaff at ICML 2012. Second, I will talk about our work in data stream mining with a focus on classification within the Massive Online Analysis framework MOA. After a quick overview of what is in MOA I will present two recent results that indicate a need for caution and a statement of what constitutes state-of-the-art in data stream classification for practitioners. Finally, I will present the idea of experiment databases, a framework for machine learning experimentation that saves effort and offers opportunities for meta learning and hypothesis generation.

See Also:

Download slides icon Download slides: acml2013_holmes_weka_moa_databases_01.pdf (30.0┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: