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- ArticleDecember 2010
Active Learning from Multiple Noisy Labelers with Varied Costs
ICDM '10: Proceedings of the 2010 IEEE International Conference on Data MiningPages 639–648https://doi.org/10.1109/ICDM.2010.147In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, and that this labeler is noise-free. In reality, it is possible that ...
- research-articleJune 2009
Efficiently learning the accuracy of labeling sources for selective sampling
KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data miningPages 259–268https://doi.org/10.1145/1557019.1557053Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources ('oracles' or 'experts') with different but unknown reliabilities? With the recent ...