Semisupervised Learning Approaches
author:Tom Mitchell,
School of Computer Science, Carnegie Mellon University
published: Feb. 25, 2007, recorded: September 2006, views: 6906
published: Feb. 25, 2007, recorded: September 2006, views: 6906
You might be experiencing some problems with Your Video player.
Slides
Related content
Visitors who watched this lecture also watched...
04:59:19
21384 views - Sam Roweis, 2006
08:36
4834 views - Davor Orlič, Tom Mitchell, 2006
03:54:31
15399 views - Chih-Jen Lin, 2006
05:02:23
9031 views - John Shawe-Taylor, 2004
55:47
4312 views - William Cohen, 2006
01:00:47
15055 views - David MacKay, 2006
01:01:41
2778 views - Kamal Nigam, 2006
01:06:55
6899 views - Fei-Fei Li, 2006
01:17:48
7548 views - Isabelle Guyon, 2007
04:38
3774 views - Davor Orlič, Fei-Fei Li, 2006
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.
We are currently conducting a short survey. We value your feedback, and would appreciate if you took a few moments to respond to some questions. Click here to take the survey.
We are currently conducting a short survey. We value your feedback, and would appreciate if you took a few moments to respond to some questions. Click here to take the survey.
See Also:
Launch in a standalone WM Player
Switch to Windows Media Player
Download slides:
mlas06_mitchell_sla.ppt (2.9 MB)
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !



Reviews and comments:
Really a nice lecture!
Thanks a lot for uploading it.
hmm....very interesting for first timers in semi-supervised learning, but i was also expecting more on worse case scenario in both EM and Co-Training approaches he discussed. Wished he had more time to speak, still a great start....
The talk was very well done. The use of co-training seems artificial to me, however. By segregating the feature sets into 2 groups, no new information has been created. Why not just train a classifier using all of the features? It's not like the labels are only known for one classifier or the other. If that was true, there wouldn't be any way for classifier one to help classifier two.
It was a very nice and understandable lecture.
I would like to know how one actually finds the groups of redundant predictive features. In theory there could be more than two groups, which means that you may extend the co-training in dimensionality. I cannot estimate although if that might help get a way better accuracy.
I also expected to learn more for the worst case scenarios..
The slides seem to be corrupt. It is not opening. Kindly address this issue.
Excellent lecture, Tom Mitchell rocks!
Write your own review or comment: