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Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision

(CS 528)

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Convexity and prediction problems

Peter Bartlett

Division of Computer Science and Department of Statistics

UC Berkeley
Monday, Oct. 20, 2003, 4:15PM

TCSeq 200

`http://graphics.stanford.edu/ba-colloquium/`

#### Abstract

In pattern classification and other prediction problems, two families of
algorithms have been successfully applied in a broad variety of areas: kernel
methods and boosting algorithms. Both approaches optimize a convex criterion
on a convex function space. In addition to its computational advantages,
convexity has statistical advantages. This talk will review boosting and
kernel methods, and present error bounds for these methods, in terms of a
measure of complexity of the function class called the Rademacher averages. In
particular, we show that convexity leads to a better estimation rate, the rate
at which the error approaches its optimal value. In pattern classification,
the natural loss function is the indicator function for a misclassification,
which is not convex. Kernel and boosting methods for pattern classification
substitute a convex function for the discrete loss function. We show how this
substitution affects the performance of these methods.
#### About the Speaker

Peter Bartlett is a professor in the Division of Computer Science and
Department of Statistics at the University of California at Berkeley. He is
the co-author, with Martin Anthony, of the book Learning in Neural Networks:
Theoretical Foundations. He has served as an associate editor of the journals
Machine Learning, Mathematics of Control Signals and Systems, the Journal of
Machine Learning Research, and the Journal of Artificial Intelligence Research.
In 2001, he was awarded the Malcolm McIntosh Prize for Physical Scientist of
the Year in Australia, for his work in statistical learning theory. He was a
fellow, senior fellow and professor in the Australian National University's
Institute for Advanced Studies (1993-2003), and Miller Institute Visiting
Research Professor in Statistics and Computer Science at U.C. Berkeley in Fall
2001. His research interests include machine learning, statistical learning
theory, and adaptive control.

*Contact: *`bac-coordinators@cs.stanford.edu`
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