Boosting
author: Robert Schapire,
Department of Computer Science, Princeton University
published: Feb. 25, 2007, recorded: May 2005, views: 88683
published: Feb. 25, 2007, recorded: May 2005, views: 88683
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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:
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.
nice introduction! easy to follow!
Very good and informative!
Very good tutorial to get the main concepts of boosting.
First part suffices if you just want to main concept.
What a great tutorial ... Schapire is a great lecturer and teacher. Wish he was close by!
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.
@ Mehran
?
Awesome Video, The way he explains boosting is really lucid.
Thank you!
Thanks Professor.
Thanks alot for such good explanation on Ada Boost.
I think the toy example , error is calculated wrong
I am sorry, the toy example calculation is correct, sorry for my wrong comments.
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