Semisupervised Learning Approaches

author: Tom Mitchell, Machine Learning Department, School of Computer Science, Carnegie Mellon University
published: Feb. 25, 2007,   recorded: September 2006,   views: 84956
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Reviews and comments:

Comment1 weiwei_kebi_marburg, January 5, 2008 at 6:17 p.m.:

Really a nice lecture!

Thanks a lot for uploading it.


Comment2 Atif Abdul-Rahman, February 12, 2008 at 11:02 p.m.:

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....


Comment3 Tim Graettinger, April 29, 2008 at 3:54 a.m.:

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.


Comment4 Petko, July 10, 2008 at 12:37 p.m.:

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..


Comment5 newton, March 16, 2009 at 10:21 a.m.:

The slides seem to be corrupt. It is not opening. Kindly address this issue.


Comment6 A Chess, January 3, 2010 at 4:55 a.m.:

Excellent lecture, Tom Mitchell rocks!


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Please, the last 20 minutes without any sound, the lecture is amazing

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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.

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