Understanding Domain Adaptation Learning - the good and the not so good

author: Shai Ben-David, David R. Cheriton School of Computer Science, University of Waterloo
published: Oct. 6, 2014,   recorded: December 2013,   views: 137
Categories

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.
  Delicious Bibliography

Description

How can the learning of some target task benefit from training data generated by a different, yet related, task? In the past few years, a range of machine learning applications led to the development of various heuristic paradigms that address these domain adaptation and transfer learning issues. Such paradigms extend well beyond the scope of the currently available analysis. How should this a gap be addressed? I will survey some major algorithmic paradigms that have been proposed to address the transfer/adaptation learning and discuss the current theoretical understanding of these approaches. I also wish to touch upon what I view as useful vs not so insightful culture of research addressing this challenge.

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: