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Sayin, Burcu (author), Krivosheev, Evgeny (author), Yang, J. (author), Passerini, Andrea (author), Casati, Fabio (author)
Training data creation is increasingly a key bottleneck for developing machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by selecting the most informative instances for labeling. Labels in real applications are often collected from crowdsourcing, which engages...
journal article 2021
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Yang, Y. (author), Loog, M. (author)
Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to as maximizing variance...
journal article 2018
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Yang, Y. (author), Loog, M. (author)
Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regression and discuss and illustrate their underlying characteristics. Experiments are carried out on three synthetic datasets and 44 real-world datasets, providing...
journal article 2018