ML Anthology

EM-DD: An Improved Multiple-Instance Learning Technique

Qi Zhang, Sally A. Goldman
NeurIPS 2001 pp. 1073-1080
/neurips/2001/zhang2001neurips-emdd/

Abstract

We present a new multiple-instance (MI) learning technique (EM(cid:173) DD) that combines EM with the diverse density (DD) algorithm. EM-DD is a general-purpose MI algorithm that can be applied with boolean or real-value labels and makes real-value predictions. On the boolean Musk benchmarks, the EM-DD algorithm without any tuning significantly outperforms all previous algorithms. EM-DD is relatively insensitive to the number of relevant attributes in the data set and scales up well to large bag sizes. Furthermore, EM(cid:173) DD provides a new framework for MI learning, in which the MI problem is converted to a single-instance setting by using EM to estimate the instance responsible for the label of the bag.

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Cite

Text

Zhang and Goldman. "EM-DD: An Improved Multiple-Instance Learning Technique." Neural Information Processing Systems, 2001.

Markdown

[Zhang and Goldman. "EM-DD: An Improved Multiple-Instance Learning Technique." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/zhang2001neurips-emdd/)

BibTeX

@inproceedings{zhang2001neurips-emdd,
  title     = {{EM-DD: An Improved Multiple-Instance Learning Technique}},
  author    = {Zhang, Qi and Goldman, Sally A.},
  booktitle = {Neural Information Processing Systems},
  year      = {2001},
  pages     = {1073-1080},
  url       = {https://mlanthology.org/neurips/2001/zhang2001neurips-emdd/}
}