Active Learning for Crowd-Sourced Databases

  title={Active Learning for Crowd-Sourced Databases},
  author={Barzan Mozafari and Purnamrita Sarkar and Michael J. Franklin and Michael I. Jordan and Samuel Madden},
Crowd-sourcing has become a popular means of acquiring labeled data for many tasks where humans are more accurate than computers, such as image tagging, entity resolution, or sentiment analysis. However, due to the time and cost of human labor, solutions that solely rely on crowd-sourcing are often limited to small datasets (i.e., a few thousand items). This paper proposes algorithms for integrating machine learning into crowd-sourced databases in order to combine the accuracy of human labeling… CONTINUE READING
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