The original article [1] contains an error whereby the author, Frederico G Romero's name is displayed incorrectly; the correct spelling is instead displayed in this Correction article.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6260736 | PMC |
| http://dx.doi.org/10.1186/s12970-018-0261-8 | DOI Listing |
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Partial multilabel learning (PML) aims to learn from training data, where each instance is associated with a set of candidate labels, among which only a part is correct. The common strategy to deal with such a problem is disambiguation, that is, identifying the ground-truth labels from the given candidate labels. However, the existing PML approaches always focus on leveraging the instance relationship to disambiguate the given noisy label space, while the potentially useful information in label space is not effectively explored.
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The original article [1] contains an error whereby the author, Frederico G Romero's name is displayed incorrectly; the correct spelling is instead displayed in this Correction article.
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