Corpus ID: 15908955

Learning from the Wisdom of Crowds by Minimax Entropy

@inproceedings{Zhou2012LearningFT,
  title={Learning from the Wisdom of Crowds by Minimax Entropy},
  author={Dengyong Zhou and John C. Platt and Sumit Basu and Yi Mao},
  booktitle={NIPS},
  year={2012}
}

Dengyong Zhou, John C. Platt, +1 author Yi Mao

Published in NIPS 2012

Mathematics, Computer Science

An important way to make large training sets is to gather noisy labels from crowds of nonexperts. We propose a minimax entropy principle to improve the quality of these labels. Our method assumes that labels are generated by a probability distribution over workers, items, and labels. By maximizing the entropy of this distribution, the method naturally infers item confusability and worker expertise. We infer the ground truth by minimizing the entropy of this distribution, which we show minimizes… CONTINUE READING

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