Estimating Accuracy from Unlabeled Data

  title={Estimating Accuracy from Unlabeled Data},
  author={Emmanouil Antonios Platanios and Avrim Blum and Tom M. Mitchell},
We consider the question of how unlabeled data can be used to estimate the true accuracy of learned classifiers. This is an important question for any autonomous learning system that must estimate its accuracy without supervision, and also when classifiers trained from one data distribution must be applied to a new distribution (e.g., document classifiers trained on one text corpus are to be applied to a second corpus). We first show how to estimate error rates exactly from unlabeled data when… CONTINUE READING
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