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Undid revision 1176600427 by 128.30.9.215 (talk)Reverted unexplained edit. "Query strategies" is more general than "Sampling methods" and not all of these strategies are sampling methods. |
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*'''Membership Query Synthesis''': This is where the learner generates its own instance from an underlying natural distribution. For example, if the dataset are pictures of humans and animals, the learner could send a clipped image of a leg to the teacher and query if this appendage belongs to an animal or human. This is particularly useful if the dataset is small.<ref>{{Cite journal|last1=Wang|first1=Liantao|last2=Hu|first2=Xuelei|last3=Yuan|first3=Bo|last4=Lu|first4=Jianfeng|date=2015-01-05|title=Active learning via query synthesis and nearest neighbour search|url=http://espace.library.uq.edu.au/view/UQ:344582/UQ344582_OA.pdf|journal=Neurocomputing|volume=147|pages=426–434|doi=10.1016/j.neucom.2014.06.042|s2cid=3027214 }}</ref>
*'''Pool-Based Sampling''': In this scenario, instances are drawn from the entire data pool and assigned a confidence score, a measurement of how well the learner
*'''Stream-Based Selective Sampling''': Here, each unlabeled data point is examined one at a time with the machine evaluating the informativeness of each item against its query parameters. The learner decides for itself whether to assign a label or query the teacher for each datapoint.
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