This article is about a machine learning method. For active learning in the context of education, see active learning. In statistics literature it is sometimes also called optimal experimental design. There are foundations of machine learning pdf download in which unlabeled data is abundant but manually labeling is expensive.
This type of iterative supervised learning is called active learning. Since the learner chooses the examples, the number of examples to learn a concept can often be much lower than the number required in normal supervised learning.
With this approach, there is a risk that the algorithm be overwhelmed by uninformative examples. Data points where the label is known. Data points where the label is unknown.
Balance exploration and exploitation: the choice of examples to label is seen as a dilemma between the exploration and the exploitation over the data space representation. This strategy manages this compromise by modelling the active learning problem as a contextual bandit problem.