Salīdzināt metodes

	Aktīvā mācīšanās	Konformālā prognozēšana
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2009	2005
Autors	Burr Settles	Vovk, Gammerman & Shafer
Tips	Interactive supervised learning framework	Distribution-free uncertainty quantification framework
Pirmavots	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic Learning in a Random World. Springer. ISBN: 978-0-387-00152-4
Citi nosaukumi	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	Conformal Inference, Conformal Risk Control, Inductive Conformal Prediction, Uyumsal Tahmin
Saistītās	2	2
Kopsavilkums	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.	Conformal Prediction is a distribution-free framework for constructing statistically valid prediction sets (for classification) or prediction intervals (for regression) around the output of any pre-trained machine learning model. Introduced by Vovk, Gammerman, and Shafer in their 2005 monograph, it provides a finite-sample marginal coverage guarantee — the true label falls inside the prediction set with at least 1-alpha probability — without requiring parametric assumptions about the data distribution.