Broad Area Colloquium for Artificial Intelligence,
Geometry, Graphics, Robotics and Vision
The Boosting Approach to Machine Learning
Robert Schapire
AT&T Labs
Monday, April 29th, 2002, 4:45PM
Gates B01 http://robotics.stanford.edu/ba-colloquium/
Abstract
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, including an
application to a human-computer spoken-dialogue system.
About the Speaker
Robert Schapire received his ScB in math and computer science from Brown
University in 1986, and his SM (1988) and PhD (1991) from MIT under the
supervision of Ronald Rivest. His dissertation on "The Design and
Analysis of Efficient Learning Algorithms" won the 1991 ACM Doctoral
Dissertation Award. After a short post-doc at Harvard, Rob became a
member of technical staff at AT&T Labs (formerly AT&T Bell Laboratories)
where he has been since 1991. His main research interest is in
theoretical and applied machine learning.
Contact: bac-coordinators@cs.stanford.edu
