Maximum Margin Coresets for Active and Noise Tolerant Learning
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 836-840.
Abstract
We study the problem of learning large margin halfspaces in various settings using coresets and 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.
@inproceedings{ijcai2007p134,
title = {Maximum Margin Coresets for Active and Noise Tolerant Learning},
author = {Har-Peled, Sariel and Roth, Dan and Zimak, Dav},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {836--840},
year = {2007},
url = {https://ijcai.org/proceedings/2007/134},
}
TY - CPAPER TI - Maximum Margin Coresets for Active and Noise Tolerant Learning AU - Har-Peled, Sariel AU - Roth, Dan AU - Zimak, Dav T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 836 EP - 840 UR - https://ijcai.org/proceedings/2007/134 ER -Copied
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