Сравнение методов

	Онлайновое обучение метрикам	Онлайн-обучение
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2004–2009	1958–2000s
Автор метода	Shalev-Shwartz, S.; Singer, Y.; and others	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Тип	Online / incremental learning of distance metrics	Learning paradigm (sequential model update)
Основополагающий источник	Shalev-Shwartz, S., Singer, Y., & Ng, A. Y. (2004). Online and batch learning of pseudo-metrics. Proceedings of the 21st International Conference on Machine Learning (ICML 2004), pp. 94. ACM.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Другие названия	OML, incremental metric learning, streaming metric learning, online distance metric learning	incremental learning, sequential learning, streaming learning, online machine learning
Связанные	3	6
Сводка	Online Metric Learning adapts a Mahalanobis distance metric incrementally as new labeled examples or pairwise constraints arrive one at a time, without storing the full dataset. It merges the efficiency of online learning with the representational power of metric learning, making it suitable for streaming, large-scale, or continually changing environments where retraining from scratch is impractical.	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.