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Related Questions
- How do query-by-committee methods compare in terms of computational efficiency to other ensemble methods, such as bagging or boosting?
- What are the primary factors that contribute to the computational cost of implementing query-by-committee methods, and how can they be mitigated?
- Can you provide an example of a real-world application where query-by-committee methods were used to optimize computational efficiency?
- How do the computational costs of query-by-committee methods scale with the size of the training dataset and model complexity?
- Are there any parallelization techniques that can be employed to reduce the computational cost of query-by-committee methods?
- Can you discuss the trade-offs between increasing model accuracy and reducing computational cost in query-by-committee methods?
- What are some strategies for selecting the optimal number of committee members in query-by-committee methods to balance accuracy and computational cost?
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