Date of Original Version
Copyright 2013 by the author(s)
Abstract or Description
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
Journal of Machine Learning Research : Workshop and Conference Proceedings, 28, 1112-1120.