Maximum Margin Coresets for Active and Noise Tolerant Learning

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.

• Type: Conference
• Year: 2007
• Where: IJCAI
• Authors: Sariel Har-Peled, Dan Roth, Dav Zimak
• Keywords: Artificial Intelligence, IJCAI 2007, Large Margin, Large Margin Coresets, Margin Active Learning