Active learning for electrodermal activity classification

Active learning for electrodermal activity classification

To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling p...

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Bibliographic Details
Main Authors: Xia, Victoria F. (Contributor), Jaques, Natasha Mary (Contributor), Taylor, Sara Ann (Contributor), Fedor, Szymon (Contributor), Picard, Rosalind W. (Contributor)
Other Authors: Massachusetts Institute of Technology. Media Laboratory. Affective Computing Group (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Media Laboratory (Contributor), Program in Media Arts and Sciences (Massachusetts Institute of Technology) (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2017-05-26T19:27:35Z.
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