published: Feb. 25, 2007, recorded: May 2005, views: 12905
Report a problem or upload filesIf 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.
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.
Download slides: Robert_Schapire_Boosting_corrected.ps (712.6 KB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !