based on available training data (black dots). The bottom row plots the network’s output variance across the input range, which is used to select the query for the next iteration.
2.5 DISCUSSION
Uncertainty sampling is possibly the most popular active learning strategy in practice. Perhaps this is because of its intuitive appeal combined with the ease of implementation. Most of the common uncertainty-based utility measures do not require significant engineering overhead to use. In fact, as long as the learner can provide a confidence or probability score along with its predictions, any of the measures in Section 2.3 can be employed with the learner as a “black box.” Standard classification or inference procedures can be used, leaving the choice of learning algorithm fairly modular. This is not necessarily the case for all active learning approaches. Furthermore, if inference is fast and tractable, then querying should also be fast and tractable.
Our discussion of uncertainty sampling has, thus far, been limited to the pool-based setting where the learner selects the “best” query from the set of unlabeled data U. Uncertainty sampling can also be employed in the stream-based selective sampling setting, where unlabeled instances are drawn x ∽ DX one at a time from an input distribution, and the learner decides on the spot whether to query or discard it. The simplest way to implement uncertainty-based selective sampling is to set a threshold on the uncertainty measure and use this to define a region of uncertainty. An instance is queried if it falls within the region of uncertainty, and discarded otherwise. The learner is re-trained after each new query instance (or batch of instances) is labeled and added to L.
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