Spring Seminar Series - February 18, 2003
University of Minnesota
School of Statistics
College of Liberal Arts
From margin-based classification to ψ-learning
Xiaotong Shen
Department of Statistics
The Ohio State University
Tuesday, February 18, 2003
4:00 PM, 115
Ford Hall
Minneapolis, East Bank Campus
Social at 3:30 PM, 300
Ford Hall
Abstract
The concept of large margins plays an important role in analyzing
learning methodologies such as Boosting, Neural Networks, and Support Vector
Machines. In this talk, I will present a novel classification methodology
called ψ-learning. While retaining the interpretation of large margins,
ψ-learning delivers high performance in generalization, especially
in nonseparable cases, as it is derived from a direct consideration of generalization
errors. The nonconvex minimization involved in ψ-learning is solved
via a global optimization method based on d.c. (differenced convex) programming.
The theoretical foundation of ψ-learning will be discussed, in addition
to illustrative examples.