方法对比

	主动学习	Boosting	在线学习
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2009	1990–1997	1958–2000s
提出者	Burr Settles	Schapire, R. E.; Freund, Y.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
类型	Interactive supervised learning framework	Sequential ensemble (iterative reweighting)	Learning paradigm (sequential model update)
开创性文献	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
别名	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	incremental learning, sequential learning, streaming learning, online machine learning
相关	2	6	6
摘要	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.