Active learning with multiple views

  • Muslea I. M
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Active learners alleviate the burden of labeling large amounts of
data by detecting and asking the user to label only the most informative
examples in the domain. We focus here on active learning for multi-view
domains, in which there are several disjoint subsets of features
(views), each of which is sufficient to learn the target concept.
In this paper we make several contributions. First, we introduce
Co-Testing, which is the first approach to multi-view active learning.
Second, we extend the multi-view learning framework by also exploiting
weak views, which are adequate only for learning a concept that is
more general/specific than the target concept. Finally, we empirically
show that Co-Testing outperforms existing active learners on a variety
of real world domains such as wrapper induction, Web page classification,
advertisement removal, and discourse tree parsing. 漏 2006 AI Access
Foundation. All rights reserved.

Author-supplied keywords

  • Active learning; Advertisement removal; Multi-vie
  • Classification (of information); Data processing;
  • Learning systems

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  • Minton S Knoblock C A Muslea I.

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