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Related Questions
- Can you explain the process of selecting a suitable regularization technique for semi-supervised learning?
- How does transfer learning and knowledge distillation contribute to leveraging unlabeled data?
- What are the potential challenges of semi-supervised learning when dealing with high-dimensional data?
- How can you address class imbalance issues in semi-supervised learning using undersampling and oversampling methods?
- Can you describe the differences between label propagation, graph-based semi-supervised learning, and ensemble methods?
- In what ways can unlabeled data be used to prevent overfitting, such as by using co-training or multi-view learning?
- How does semi-supervised learning benefit from incorporating labeled and unlabeled data using active learning methods?
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