Active learning http://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29

*Active learning*is a form ofsupervised machine learning http://en.wikipedia.org/wiki/Supervised_learningin which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points. In statistics literature it is sometimes also calledoptimal experimental design http://en.wikipedia.org/wiki/Optimal_experimental_design.^[1] http://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29#cite_note-settles-1 ^[2] http://en.wikipedia.org/wiki/Active_learning_%28machine_learning%29#cite_note-olsson-2

There are situations in which unlabeled data is abundant but manually labeling it is expensive. In such a scenario, learning algorithms can actively query the user/teacher for labels. 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.

Voici la référence de l'article de POPL:

*Synthesis of biological models from mutation experiments*

http://dl.acm.org/citation.cfm?id=2429125

Erick