Distribution Kernel Methods for Multiple-Instance Learning
Abstract
I propose to investigate learning in the multiple-instance (MI) framework as a problem of learning from distributions. In many MI applications, bags of instances can be thought of as samples from bag-generating distributions. Recent kernel approaches for learning from distributions have the potential to be successfully applied to these domains and other MI learning problems. Understanding when distribution-based techniques work for MI learning will lead to new theoretical insights, improved algorithms, and more accurate solutions for real-world problems.
Cite
Text
Doran. "Distribution Kernel Methods for Multiple-Instance Learning." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8501Markdown
[Doran. "Distribution Kernel Methods for Multiple-Instance Learning." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/doran2013aaai-distribution/) doi:10.1609/AAAI.V27I1.8501BibTeX
@inproceedings{doran2013aaai-distribution,
title = {{Distribution Kernel Methods for Multiple-Instance Learning}},
author = {Doran, Gary},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2013},
pages = {1660-1661},
doi = {10.1609/AAAI.V27I1.8501},
url = {https://mlanthology.org/aaai/2013/doran2013aaai-distribution/}
}