Iterative Labeling for Semi-Supervised Learning

Hanneke, Steve; Roth, Dan

Permalink

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

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

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

Permalink

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

Copyright and License 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).

Owning Collections