A Boosting Approach to Multiple Instance Learning
Abstract
In this paper we present a boosting approach to multiple instance learning. As weak hypotheses we use balls (with respect to various metrics) centered at instances of positive bags. For the ∞-norm these hypotheses can be modified into hyper-rectangles by a greedy algorithm. Our approach includes a stopping criterion for the algorithm based on estimates for the generalization error. These estimates can also be used to choose a preferable metric and data normalization. Compared to other approaches our algorithm delivers improved or at least competitive results on several multiple instance benchmark data sets.
Cite
Text
Auer and Ortner. "A Boosting Approach to Multiple Instance Learning." European Conference on Machine Learning, 2004. doi:10.1007/978-3-540-30115-8_9Markdown
[Auer and Ortner. "A Boosting Approach to Multiple Instance Learning." European Conference on Machine Learning, 2004.](https://mlanthology.org/ecmlpkdd/2004/auer2004ecml-boosting/) doi:10.1007/978-3-540-30115-8_9BibTeX
@inproceedings{auer2004ecml-boosting,
title = {{A Boosting Approach to Multiple Instance Learning}},
author = {Auer, Peter and Ortner, Ronald},
booktitle = {European Conference on Machine Learning},
year = {2004},
pages = {63-74},
doi = {10.1007/978-3-540-30115-8_9},
url = {https://mlanthology.org/ecmlpkdd/2004/auer2004ecml-boosting/}
}