Exploiting Ontology Structures and Unlabeled Data for Learning
Abstract
We present and analyze a theoretical model designed to understand and explain the effectiveness of ontologies for learning multiple related tasks from primarily unlabeled data. We present both information-theoretic results as well as efficient algorithms. We show in this model that an ontology, which specifies the relationships between multiple outputs, in some cases is sufficient to completely learn a classification using a large unlabeled data source.
Cite
Text
Balcan et al. "Exploiting Ontology Structures and Unlabeled Data for Learning." International Conference on Machine Learning, 2013.Markdown
[Balcan et al. "Exploiting Ontology Structures and Unlabeled Data for Learning." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/balcan2013icml-exploiting/)BibTeX
@inproceedings{balcan2013icml-exploiting,
title = {{Exploiting Ontology Structures and Unlabeled Data for Learning}},
author = {Balcan, Nina and Blum, Avrim and Mansour, Yishay},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1112-1120},
volume = {28},
url = {https://mlanthology.org/icml/2013/balcan2013icml-exploiting/}
}