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An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels
[article]
2016
arXiv
pre-print
Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to
arXiv:1606.05725v1
fatcat:ndn5busqpffwlcjw5vcj6fheuy