The Boosting Approach to Machine Learning
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.
Seminar to be held in Room 107, 24 Hillhouse Avenue at 4:15 pm