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Exploiting Ontology Structures and Unlabeled Data for Learning

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journal contribution
posted on 01.09.2005, 00:00 by Nina Balcan, Avrim Blum, Yishay Mansour

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




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