Joint Colloquium: 

Department of Computer Science and Department of Operations Research 
Thursday March 7th, 2002 4:15pm 
Upson Hall B17

The Boosting Approach to Machine Learning

Robert Schapire

AT&T Research Labs

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. In this talk, I will introduce the boosting algorithm AdaBoost, and explain the underlying theory of boosting, including our explanation of why boosting often does not suffer from overfitting. I also will describe some recent applications and extensions of boosting.