Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning

@article{Huang2013ActiveQD,
  title={Active Query Driven by Uncertainty and Diversity for Incremental Multi-label Learning},
  author={Sheng-Jun Huang and Zuo-cheng Zhou},
  journal={2013 IEEE 13th International Conference on Data Mining},
  year={2013},
  pages={1079-1084}
}
In multi-label learning, it is rather expensive to label instances since they are simultaneously associated with multiple labels. Therefore, active learning, which reduces the labeling cost by actively querying the labels of the most valuable data, becomes particularly important for multi-label learning. A strong multi-label active learning algorithm usually consists of two crucial elements: a reasonable criterion to evaluate the gain of queried label, and an effective classification model… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 33 REFERENCES