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Maximum Margin Coresets for Active and Noise Tolerant Learning

Sariel Har-Peled, Dan Roth, and Dav Zimak.

We study the problem of learning large margin halfspaces in various settings using coresets to show that coresets are a widely applicable tool for large margin learning. A large margin coreset is a subset of the input data sufficient for approximating the true maximum margin solution. In this work, we provide a direct algorithm and analysis for constructing large margin coresets. We show various applications including a novel coreset based analysis of large margin active learning and a polynomial time (in the number of input data and the amount of noise) algorithm for agnostic learning in the presence of outlier noise. We also highlight a simple extension to multi-class classification problems and structured output learning.
Postscript, PDF.
Last modified: Tue Sep 19 16:02:55 CDT 2006