Data Subset Selection With Imperfect Multiple Labels
IEEE Transactions on Neural Networks and Learning Systems, pp. 2212-2221, 2018.
We study the problem of selecting a subset of weakly labeled data where the labels of each data instance are redundant and imperfect. In real applications, less-than-expert labels are obtained at low cost in order to acquire many labels for each instance and then used for estimating the ground truth. However, on one side, preparing and pr...More
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