SMILE: A Similarity-Based Approach for Multiple Instance Learning

@article{Xiao2010SMILEAS,
  title={SMILE: A Similarity-Based Approach for Multiple Instance Learning},
  author={Yanshan Xiao and Bo Liu and Longbing Cao and Jie Yin and Xindong Wu},
  journal={2010 IEEE International Conference on Data Mining},
  year={2010},
  pages={589-598}
}
Multiple instance learning (MIL) is a generalization of supervised learning which attempts to learn useful information from bags of instances. In MIL, the true labels of the instances in positive bags are not always available for training. This leads to a critical challenge, namely, handling the ambiguity of instance labels in positive bags. To address this issue, this paper proposes a novel MIL method named SMILE (Similarity-based Multiple Instance LEarning). It introduces a similarity weight… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 25 references

Similar Papers

Loading similar papers…