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Active Learning

Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.

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Active Learning (Human-in-the-Loop)

Rekod kaedah taksonomik · ml-model / machine-learning

Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. · URL

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Same method familyConformal Predictionmachine-suggested · Relational suggestion, not evidence.See alsoUncertainty Quantificationmachine-suggested · Relational suggestion, not evidence.