Towards advanced machine generalization via data interplay
Traditional machine learning methodologies presuppose that training and testing datasets are Independent and Identically Distributed (IID), i.e., assuming the samples of both training and testing are drawn from a consistent distribution. However, this IID assumption often fails to hold in real-world...
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| 格式: | Thesis-Doctor of Philosophy |
| 語言: | English |
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Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/180357 |
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