Active Learning

Active learning for Electrodermal Activity classification
We use labels provided by domain experts to classify whether artifacts are present in an Electrodermal Activity signal. Through the use of active learning, we improve sample efficiency and reduce the burden on human experts by as much as 84%, while offering the same or improved performance.
V. Xia, Natasha Jaques, S. Taylor, S. Fedor, R. Picard
2015 In IEEE Conference on Signal Processing in Medicine and Biology (SPMB)
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Active learning for Electrodermal Activity classification