Corpus ID: 2081034

Active Learning for Crowd-Sourced Databases

@article{Mozafari2012ActiveLF,
  title={Active Learning for Crowd-Sourced Databases},
  author={Barzan Mozafari and Purnamrita Sarkar and Michael J. Franklin and Michael I. Jordan and Samuel Madden},
  journal={ArXiv},
  year={2012},
  volume={abs/1209.3686}
}
  • Barzan Mozafari, Purnamrita Sarkar, +2 authors Samuel Madden
  • Published 2012
  • Computer Science
  • ArXiv
  • Crowd-sourcing has become a popular means of acquiring labeled data for a wide variety of tasks where humans are more accurate than computers, e.g., labeling images, matching objects, or analyzing sentiment. [...] Key MethodBased on this observation, we present two new active learning algorithms to combine humans and algorithms together in a crowd-sourced database. Our algorithms are based on the theory of non-parametric bootstrap, which makes our results applicable to a broad class of machine learning models…Expand Abstract

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