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Related Questions
- What are the key assumptions and limitations of entropy-based methods for query selection in active learning?
- How do distance-based methods, such as k-NN, differ from entropy-based methods in terms of query selection criteria?
- Can you provide examples of scenarios where entropy-based methods are more suitable than distance-based methods, and vice versa?
- How do the choice of distance metric and the number of nearest neighbors affect the performance of distance-based methods?
- What are some common techniques used to combine entropy-based and distance-based methods for improved query selection?
- How do active learning algorithms, such as core-set selection, use query selection methods to optimize the labeling process?
- What are some common applications of active learning in real-world domains, such as image classification, natural language processing, and recommender systems?
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