Regularized Minimax Conditional Entropy for Crowdsourcing

  title={Regularized Minimax Conditional Entropy for Crowdsourcing},
  author={Dengyong Zhou and Qiang Liu and John C. Platt and Christopher Meek and Nihar B. Shah},
There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we derive a unique probabilistic labeling model jointly parameterized by worker ability and item… CONTINUE READING
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