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Dual Strategy Active Learning
Computer Science Department
  • Pinar Donmez, Carnegie Mellon University
  • Jaime G. Carbonell, Carnegie Mellon University
  • Paul N Bennett, Microsoft Research
Date of Original Version
Conference Proceeding
Abstract or Description
Active Learning methods rely on static strategies for sampling unlabeled point(s). These strategies range from uncertainty sampling and density estimation to multi-factor methods with learn-once-use- always model parameters. This paper proposes a dynamic approach, called DUAL, where the strategy selection parameters are adaptively updated based on estimated future residual error reduction after each actively sampled point. The objective of dual is to outperform static strategies over a large operating range: from very few to very many labeled points. Empirical results over six datasets demonstrate that DUAL outperforms several state-of-the-art methods on most datasets.
Citation Information
Pinar Donmez, Jaime G. Carbonell and Paul N Bennett. "Dual Strategy Active Learning" (2007)
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