Theory and Applications of Boosting

author: Robert Schapire, Department of Computer Science, Princeton University
published: July 30, 2009,   recorded: June 2009,   views: 24123


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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 practical applications and extensions of boosting will also be described.

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Reviews and comments:

Comment1 kaneperry, August 3, 2009 at 1:46 a.m.:

why i can't play it!"Server not found:rtmp://"

Comment2 Alok Singh, September 2, 2016 at 10:14 p.m.:

Very good explanation and different views on Information geometry , game theory and loss minimization

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