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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.

🚀 Conference Pioneer — ICML 2013
🧭 Keyword Pioneer — ontology structure
🐣 Hot Topic Early Bird — semi-supervised learning
🐝 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
🌉 Interdisciplinary Bridge — Knowledge & Reasoning and Machine Learning

Authors

Nina Balcan , Avrim Blum , Yishay Mansour

Topics

Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Optimization & Theory > Learning Theory Knowledge & Reasoning > Representation > Knowledge Graphs Machine Learning > Learning Paradigms > Transfer Learning

Keywords

information theory semi-supervised learning multi-task learning unlabeled datum ontology structure
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