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Supervised Learning from Multiple Experts: Whom to Trust When Everyone Lies a Bit
This video was recorded at 26th International Conference on Machine Learning (ICML), Montreal 2009. We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method clearly beats the commonly used majority voting baseline.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
