Active learning setting（English）

1. In many potential applications of machine learning, unlabeled data are abundantly available at low cost, but there is a paucity of labeled data, and labeling unlabeled examples is expensive and / or time-consuming. 

2. As a result, large quantities of unlabeled data are often available at little or no cost, but obtaining more than a comparatively small amount of labeled data is prohibitively expensive or time consuming. 

3. Active learning aims to address this problem by constructing algorithms that are able to guide the labeling of a small amount of data, such that the generalization ability of the classifier is maximized whilst minimizing the use of the oracle.

4.  Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain.