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Related Questions
- What are the key considerations for determining the optimal query strategy in active learning to minimize uncertainty and maximize informative sampling?
- How does the choice of uncertainty measure (e.g., entropy, margin) impact the trade-off between reducing uncertainty and increasing sample size in active learning?
- Can you explain the concept of 'optimism' in active learning and how it relates to the balance between uncertainty reduction and sample size in improving model generalizability?
- What are some common pitfalls to avoid when implementing active learning to balance uncertainty reduction and sample size, and how can they be mitigated?
- In what scenarios is it beneficial to prioritize uncertainty reduction over sample size, and vice versa, in active learning for improving model generalizability?
- Can you discuss the relationship between the number of iterations, sample size, and uncertainty reduction in active learning, and how they influence model generalizability?
- How do techniques like transfer learning and domain adaptation impact the optimal balance between uncertainty reduction and sample size in active learning for improving model generalizability?
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