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• W2971383126 abstract "Various strategies for active learning have been proposed in the machine learning literature. In uncertainty which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are almost exclusively of a probabilistic nature. In this paper, we advocate a distinction between two different types of uncertainty, referred to as and aleatoric, in the context of active learning. Roughly speaking, these notions capture the reducible and the irreducible part of the total uncertainty in a prediction, respectively. We conjecture that, in uncertainty the usefulness of an instance is better reflected by its than by its aleatoric uncertainty. This leads us to suggest the principle of epistemic uncertainty sampling, which we instantiate by means of a concrete approach for measuring and aleatoric uncertainty. In experimental studies, uncertainty sampling does indeed show promising performance." @default.
• W2971383126 created "2019-09-12" @default.
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• W2971383126 date "2019-08-31" @default.
• W2971383126 modified "2023-10-01" @default.
• W2971383126 title "Epistemic Uncertainty Sampling" @default.
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