An initial parameter search for rapid concept drift adaptation in deep neural networks
Concept drift is a common issue in data stream mining algorithms that causes prediction models to lose its original performance gradually or abruptly due to the non-stationarity of the data distribution and decision boundaries. To combat concept drifts, prediction models need to be updated periodica...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
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2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/98055/ http://dx.doi.org/10.1007/978-3-030-73689-7_4 |
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