Active learning (machine learning): Difference between revisions

Revision as of 03:39, 23 July 2023

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{{about|a machine learning method|active learning in the context of education|active learning}}

{{Machine learning bar}}

'''Active learning''' is a special case of [[machine learning]] in which a learning algorithm can interactively query a user (or some other information source) to label new data points with the desired outputs.<ref name="settles">{{cite documentweb

| title = Active Learning Literature Survey

| url = http://pages.cs.wisc.edu/~bsettles/pub/settles.activelearning.pdf

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|isbn=978-1-5090-5473-2

|s2cid=15285595

}}</ref> In statistics literature, it is sometimes also called [[optimal experimental design]].<ref name="olsson">{{cite documentweb | url=http://eprints.sics.se/3600/ | title=A literature survey of active machine learning in the context of natural language processing |series=SICS Technical Report T2009:06 | author=Olsson, Fredrik| date=April 2009 }}</ref> The information source is also called ''teacher'' or ''oracle''.

There are situations in which unlabeled data is abundant but manual labeling 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 is overwhelmed by uninformative examples. Recent developments are dedicated to multi-label active learning,<ref name="multi"/> hybrid active learning<ref name="hybrid"/> and active learning in a single-pass (on-line) context,<ref name="single-pass"/> combining concepts from the field of machine learning (e.g. conflict and ignorance) with adaptive, [[incremental learning]] policies in the field of [[online machine learning]].