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Related Questions
- What are the key factors to consider when designing a query strategy for active learning to ensure the most informative samples are selected for labeling?
- How can uncertainty-based sampling methods, such as entropy-based or margin-based methods, be used to select the most informative samples?
- What are the trade-offs between exploration-exploitation strategies in active learning, and how can they be balanced to optimize the selection of informative samples?
- How can the concept of representativeness be used to select samples that are representative of the underlying distribution of the data?
- What are the benefits and limitations of using distance-based sampling methods, such as k-nearest neighbors, to select the most informative samples?
- How can the concept of diversity be used to select samples that cover diverse regions of the input space?
- What are some common metrics used to evaluate the performance of active learning algorithms in selecting the most informative samples?
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