The active learning model is motivated by scenarios in which it is easy to amass vast quantities of unlabeled data (images and videos off the web, speech signals from microphone recordings, and so on) but costly to obtain their labels. Like supervised learning, the goal is ultimately to learn a classifier. But like unsupervised learning, the data come unlabeled. More precisely, the labels are hidden, and each of them can be revealed only at a cost. The idea is to query the labels of just a few points that are especially informative about the decision boundary, and thereby to obtain an accurate classifier at significantly lower cost than regular supervised learning. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Dasgupta, S. (2009). The two faces of active learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5808 LNAI, p. 35). https://doi.org/10.1007/978-3-642-04747-3_5
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