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Toward Optimal Probabilistic Active Learning Using a Bayesian Approach
[article]
2020
arXiv
pre-print
Gathering labeled data to train well-performing machine learning models is one of the critical challenges in many applications. Active learning aims at reducing the labeling costs by an efficient and effective allocation of costly labeling resources. In this article, we propose a decision-theoretic selection strategy that (1) directly optimizes the gain in misclassification error, and (2) uses a Bayesian approach by introducing a conjugate prior distribution to determine the class posterior to
arXiv:2006.01732v1
fatcat:qjuo3lbvtvcp3pl6ispfhe33uu