Improving machine learning methods for solving non-stationary conditions based on data availability, time urgency, and types of change
Supervised learning algorithms do not work well when the deployment condition is dissimilar to the training condition. Such non-stationary conditions include covariate shifts and concept shifts. Importance weighted learning (IWL) is used to handle a one-time covariate shift but not frequent shifts a...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/147041 |