Data Subset Selection With Imperfect Multiple Labels

IEEE Transactions on Neural Networks and Learning Systems, pp. 2212-2221, 2018.

Cited by: 2|Bibtex|Views52|DOI:https://doi.org/10.1109/TNNLS.2018.2875470
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Other Links: pubmed.ncbi.nlm.nih.gov|academic.microsoft.com|dblp.uni-trier.de

Abstract:

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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