2013
ICML
ICML 2013
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.
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Conference Pioneer
- ICML 2013
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Keyword Pioneer
- ontology structure
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Hot Topic Early Bird
- semi-supervised learning
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Cross-Pollinator
- Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Interdisciplinary Bridge
- Knowledge & Reasoning and Machine Learning