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Active Learning: Problem Settings and Recent Developments
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In supervised learning, acquiring labeled training data for a predictive model can be very costly, but acquiring a large amount of unlabeled data is often quite easy. Active learning is a method of obtaining predictive models with high precision at a limited cost through the adaptive selection of samples for labeling. This paper explains the basic problem settings of active learning and recent research trends. In particular, research on learning acquisition functions to select samples from the data for labeling, theoretical work on active learning algorithms, and stopping criteria for sequential data acquisition are highlighted. Application examples for material development and measurement are introduced.
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Cited by 2 Pith papers
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Cost-effective Reduced-Order Modeling via Bayesian Active Learning
An active learning framework for reduced-order models, BayPOD-AL, shows that an error-bounded acquisition function outperforms uncertainty sampling and random sampling on a 1D heat equation surrogate task.
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