Question sets
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This collection of questions and answers covers the fundamental concepts, algorithms, challenges, and applications of semi-supervised learning.
@qs_by_qset
Created this 6 months ago
1. Describe the graph-based approach to semi-supervised learning.
2. Describe the self-training algorithm for semi-supervised learning.
3. Discuss the ethical considerations related to using unlabeled data in semi-supervised learning.
4. What are the applications of semi-supervised learning?
5. Explain the difference between supervised and semi-supervised learning.
6. Explain the role of regularization in semi-supervised learning.
7. Discuss the challenges associated with using unlabeled data in semi-supervised learning.
8. What are the assumptions and limitations of semi-supervised learning?
9. What are the benefits of using semi-supervised learning?
10. Explain the concept of co-training in semi-supervised learning.