Imagine you need to classify documents but have only a few labeled examples. How would you leverage semi-supervised learning in this scenario?
- Combine trial and error approaches
- Use clustering exclusively
- Utilize both labeled and unlabeled data
- Utilize only the labeled data
In this scenario, Semi-Supervised Learning would leverage both the limited labeled examples and the abundant unlabeled data to create an effective classification model.
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