A Similarity-Based Classification Framework for Multiple-Instance Learning

@article{Xiao2014ASC,
  title={A Similarity-Based Classification Framework for Multiple-Instance Learning},
  author={Yanshan Xiao and Bo Liu and Zhifeng Hao and Longbing Cao},
  journal={IEEE Transactions on Cybernetics},
  year={2014},
  volume={44},
  pages={500-515}
}
Multiple-instance learning (MIL) is a generalization of supervised learning that attempts to learn useful information from bags of instances. In MIL, the true labels of instances in positive bags are not available for training. This leads to a critical challenge, namely, handling the instances of which the labels are ambiguous (ambiguous instances). To deal with these ambiguous instances, we propose a novel MIL approach, called similarity-based multiple-instance learning (SMILE). Instead of… CONTINUE READING
Highly Cited
This paper has 47 citations. REVIEW CITATIONS
8 Citations
50 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 50 references

Similar Papers

Loading similar papers…