Learning From Crowds

  title={Learning From Crowds},
  author={Vikas C. Raykar and Shipeng Yu and Linda H. Zhao and Gerardo Hermosillo and Charles Florin and Luca Bogoni and Linda Moy},
  journal={Journal of Machine Learning Research},
For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. We describe a probabilistic approach for supervised learning when we have multiple… CONTINUE READING
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