Machine learningInteractive ML
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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Sources
- Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648. link ↗
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Referenced by
Active learning Association rulesActive learning Decision treeActive Learning Federated LearningActive learning Gaussian processActive Learning Gradient BoostingActive learning Isolation forestActive learning K-nearest neighborsActive Learning LightGBMActive learning One-class SVMActive Learning Self-supervised LearningActive learning Stacking ensembleActive Learning Voting EnsembleBayesian Active LearningCurriculum LearningEnsemble Active LearningEnsemble Online LearningOnline Active learningOnline LearningOnline Semi-supervised learningRobust Active LearningRobust Online LearningSelf-supervised Active LearningSemi-supervised Active LearningSemi-supervised K-meansSemi-supervised LearningSemi-supervised Online Learning