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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title From active towards InterActive learning: using consideration information to improve labeling correctness
Organization Unit
  • Abraham Bernstein
  • Jiwen Li
  • Paul Bennett
  • Raman Chandrasekar
  • Max Chickering
  • Panos Ipeirotis
  • Edith Law
  • Anton Mityagin
  • Provost Foster
  • Luis von Alm
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Page Range 40 - 43
Event Title Human Computation Workshop
Event Type workshop
Event Location Paris, France
Event Start Date June 1 - 2009
Event End Date June 1 - 2009
Abstract Text Active learning methods have been proposed to reduce the labeling effort of human experts: based on the initially available labeled instances and information about the unlabeled data those algorithms choose only the most informative instances for labeling. They have been shown to significantly reduce the size of the required labeled dataset to generate a precise model [17]. However, active learning framework assumes "perfect" labelers, which is not true in practice (e.g., [22, 23]). In particular, an empirical study for hand-written digit recognition [5] has shown that active learning works poorly when a human labeler is used. Thus, as active learning enters the realm of practical applications, it will need to confront the practicalities and inaccuracies of human expert decision-making. Specifically, active learning approaches will have to deal with the problem that human experts are likely to make mistakes when labeling the selected instances.
Digital Object Identifier 10.1145/1600150.1600165
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