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In graph-based active learning, algorithms based on expected error minimization (EEM) have been popular and yield good empirical performance.
The exact computation of EEM optimally balances exploration and exploitation.
In practice, however, EEM-based algorithms employ various approximations due to the computational hardness of exact EEM.
This can result in a lack of either exploration or exploitation, which can negatively impact the effectiveness of active learning.