Self-Training and Calibration for Learning with Limited Data
Semi-supervised learning methods such as self-training are able to leverage unlabeled data, which is widely available, as opposed to only using labeled data like many successful supervised learning methods. One part of self-training is to use a trained model to create pseudo-labels for unlabeled dat...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/144511 |