2010
NIPS
NeurIPS 2010
Online Learning: Random Averages, Combinatorial Parameters, and Learnability
Abstract
We develop a theory of online learning by defining several complexity measures. Among them are analogues of Rademacher complexity, covering numbers and fat-shattering dimension from statistical learning theory. Relationship among these complexity measures, their connection to online learning, and tools for bounding them are provided. We apply these results to various learning problems. We provide a complete characterization of online learnability in the supervised setting.
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Trend Setter
— Online Algorithms
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Keyword Pioneer
— online learning theory
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Hot Topic Early Bird
— online learning
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio