Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion

The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases... (read more)

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