Learning classifiers from dual annotation ambiguity via a minmax framework
We model two sources of labeling ambiguity effectively to construct a classifier. We modify the hinge loss to estimate true labels from multiple labelers. We extend the modified hinge loss to samples that are bags of multiple instances. The empirical results for the developed algorithms demonstrate their effectiveness.
classification data annotation ambiguity learning from multiple data annotators multiple instance learning