Boosting

author: Robert Schapire, Department of Computer Science, Princeton University
published: Feb. 25, 2007,   recorded: May 2005,   views: 8622
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Part 2 1:03:32

Description

Boosting is a general method for producing a very accurate classification rule by combining rough and moderately inaccurate "rules of thumb." While rooted in a theoretical framework of machine learning, boosting has been found to perform quite well empirically. This tutorial will introduce the boosting algorithm AdaBoost?, and explain the underlying theory of boosting, including explanations that have been given as to why boosting often does not suffer from overfitting, as well as some of the myriad other theoretical points of view that have been taken on this algorithm. Some recent applications and extensions of boosting will also be described.

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Reviews and comments:

Comment1 Pete Santago, August 7, 2007 at 7:54 p.m.:

Great talk. Very intuitive. Should have much more of this to complement papers. Speaker is excellent.

Only a small downside. Camera work could be a bit better. Either focus more on slides or speaker should move to them more often.

Thanks for this.


Comment2 Claudia, June 13, 2008 at 5:03 p.m.:

nice introduction! easy to follow!


Comment3 Oscar, August 27, 2008 at 11:21 a.m.:

Very good and informative!


Comment4 Rob, June 26, 2009 at 4:36 p.m.:

Very good tutorial to get the main concepts of boosting.
First part suffices if you just want to main concept.


Comment5 Mehran, January 30, 2010 at 1:38 a.m.:

What a great tutorial ... Schapire is a great lecturer and teacher. Wish he was close by!


Comment6 Ananth Sadanand, March 30, 2010 at 8:36 p.m.:

Very informative. Very concise and to-the-point explanation. The camera man however did a pathetic job on taping it. Very tough on the eyes to watch for 63 mins.


Comment7 Extra, January 9, 2011 at 7:36 p.m.:

@ Mehran
?


Comment8 Shreyas Saxena, July 19, 2011 at 1:44 p.m.:

Awesome Video, The way he explains boosting is really lucid.


Comment9 Yang Zhao, November 11, 2011 at 4:06 a.m.:

Thank you!

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