Efficient crowdsourcing for multi-class labeling

  title={Efficient crowdsourcing for multi-class labeling},
  author={David R. Karger and Sewoong Oh and Devavrat Shah},
Crowdsourcing systems like Amazon's Mechanical Turk have emerged as an effective large-scale human-powered platform for performing tasks in domains such as image classification, data entry, recommendation, and proofreading. Since workers are low-paid (a few cents per task) and tasks performed are monotonous, the answers obtained are noisy and hence unreliable. To obtain reliable estimates, it is essential to utilize appropriate inference algorithms (e.g. Majority voting) coupled with structured… CONTINUE READING
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Maximum likelihood estimation of observer errorrates using the em algorithm . Journal of the Royal Statistical Society

  • A. P. Dawid, A. M. Skene
  • Series C ( Applied Statistics )
  • 1979
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