Multiple Instance Learning via Disjunctive Programming Boosting
Abstract
Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classification problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learn- ing as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive programming to generate successively stronger linear relaxations of a discrete non-convex problem.
Cite
Text
Andrews and Hofmann. "Multiple Instance Learning via Disjunctive Programming Boosting." Neural Information Processing Systems, 2003.Markdown
[Andrews and Hofmann. "Multiple Instance Learning via Disjunctive Programming Boosting." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/andrews2003neurips-multiple/)BibTeX
@inproceedings{andrews2003neurips-multiple,
title = {{Multiple Instance Learning via Disjunctive Programming Boosting}},
author = {Andrews, Stuart and Hofmann, Thomas},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {65-72},
url = {https://mlanthology.org/neurips/2003/andrews2003neurips-multiple/}
}