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Title:Iterative Labeling for Semi-Supervised Learning
Author(s):Hanneke, Steve; Roth, Dan
Subject(s):Machine Learning
Natural Language Processing
Abstract:We propose a unified perspective of a large family of semi-supervised learning algorithms, which select and label unlabeled data in an iterative process. We discuss existing approaches that label examples based on the confidence of the current hypothesis, and propose an alternative approach that labels examples based on empirical risk. This new approach is shown to be statistically reasonable, allows for worst-case performance guarantees and, as we show, significantly outperforms confidence-based approaches in experiments.
Issue Date:2004-06
Genre:Technical Report
Rights Information:You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the University of Illinois at Urbana-Champaign Computer Science Department under terms that include this permission. All other rights are reserved by the author(s).
Date Available in IDEALS:2009-04-14

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