Abstract
Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of previous approaches based on discriminative learning use the margin for choosing instances. We present a method for incorporating confidence into the margin by using a newly introduced online learning algorithm and show empirically that confidence improves active learning.
Original language | English (US) |
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Pages (from-to) | 233-236 |
Number of pages | 4 |
Journal | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
DOIs | |
State | Published - 2008 |
Externally published | Yes |
Event | 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL 2008 - Columbus, United States Duration: Jun 16 2008 → Jun 17 2008 |
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics