Large Scale Learning - Challenge

author: Sören Sonnenburg, Machine Learning and Intelligent Data Analysis Group, TU Berlin
author: Vojtech Franc, Fraunhofer Institute for Intelligent Analysis and Information Systems
published: Sept. 1, 2008,   recorded: July 2008,   views: 134
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

With the exceptional increase in computing power, storage capacity and network bandwidth of the past decades, ever growing datasets are collected in fields such as bioinformatics (Splice Sites, Gene Boundaries, etc), IT-security (Network traffic) or Text-Classification (Spam vs. Non-Spam), to name but a few. While the data size growth leaves computational methods as the only viable way of dealing with data, it poses new challenges to ML methods. This workshop is concerned with the scalability and efficiency of existing ML approaches with respect to computational, memory or communication resources, e.g. resulting from a high algorithmic complexity, from the size or dimensionality of the data set, and from the trade-off between distributed resolution and communication costs.

See Also:

Download slides icon Download slides: icml08_sonnenburg_lsl_01.pdf (1.7 MB)


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: