2010
NIPS
NeurIPS 2010
Agnostic Active Learning Without Constraints
Abstract
We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, our algorithm sheds the computational burden and brittleness associated with maintaining version spaces, yet still allows for substantial improvements over supervised learning for classification.
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Hot Topic Early Bird
— active 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, Security & Privacy, Speech & Audio
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Trend Setter
— Active Learning
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Keyword Pioneer
— classification learning
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Learning Types > Active Learning
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Learning Types > Supervised Learning
Machine Learning > Learning Types > Classification
Machine Learning > Learning Paradigms > Active Learning