University of Illinois Urbana-Champaign

Iterative Labeling for Semi-Supervised Learning

Hanneke, Steve; Roth, Dan

Content Files
Iterative Labeling for Semi-Supervised Learning.pdf

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https://hdl.handle.net/2142/10818

Description

Title
Iterative Labeling for Semi-Supervised Learning
Author(s)
  • Hanneke, Steve
  • Roth, Dan
Issue Date
2004-06
Keyword(s)
  • Machine Learning
  • Natural Language Processing
Date of Ingest
2009-04-14T21:15:26Z
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.
Type of Resource
text
Genre of Resource
Technical Report
Permalink
http://hdl.handle.net/2142/10818
Copyright and License Information
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