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*'''Conformal predictors''': [[conformal prediction|This method]] predicts that a new data point will have a label similar to old data points in some specified way and degree of the similarity within the old examples is used to estimate the confidence in the prediction.<ref>{{Cite journal|last1=Makili|first1=Lázaro Emílio|last2=Sánchez|first2=Jesús A. Vega|last3=Dormido-Canto|first3=Sebastián|date=2012-10-01|title=Active Learning Using Conformal Predictors: Application to Image Classification|journal=Fusion Science and Technology|volume=62|issue=2|pages=347–355|doi=10.13182/FST12-A14626|s2cid=115384000|issn=1536-1055}}</ref>
*'''Mismatch-first farthest-traversal''': The primary selection criterion is the prediction mismatch between the current model and nearest-neighbour prediction. It targets on wrongly predicted data points. The second selection criterion is the distance to previously selected data, the farthest first. It aims at optimizing the diversity of selected data.<ref name='zhaos' />
*'''User Centered Labeling Strategies:''' Learning is accomplished by applying dimensionality reduction to graphs and figures like scatter plots. Then the user is
A wide variety of algorithms have been studied that fall into these categories.<ref name="settles" /><ref name="olsson" />
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