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Minimizing Supervision in Multi-label Categorization
The approach we adopt is one of active learning, i. e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples.
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