2006
NIPS
NeurIPS 2006
Learning from Multiple Sources
Abstract
We consider the problem of learning accurate models from multiple sources of "nearby" data. Given distinct samples from multiple data sources and estimates of the dissimilarities between these sources, we provide a general theory of which samples should be used to learn models for each source. This theory is applicable in a broad decision-theoretic learning framework, and yields results for classification and regression generally, and for density estimation within the exponential family. A key component of our approach is the development of approximate triangle inequalities for expected loss, which may be of independent interest.
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Conference Pioneer
— NIPS 2006
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Topic Pioneer
— Transfer Learning
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Transfer Learning
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Keyword Pioneer
— multi-source learning
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Hot Topic Early Bird
— transfer learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
Authors
Topics
Artificial Intelligence > Learning Paradigms > Transfer Learning
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Regression
Machine Learning > Optimization & Theory > Statistical Learning
Machine Learning > Learning Paradigms > Transfer Learning
Machine Learning > Learning Paradigms > Domain Adaptation
Machine Learning > Learning Types > Multi-Source Learning