Сравнение методов

	Онлайн-обучение	Самообучение с учителем	Перенос обучения
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1958–2000s	2018–2020	2010 (formalized); 1990s (early roots)
Автор метода	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	LeCun, Y. and community (formalized ~2018–2020)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Learning paradigm (sequential model update)	Representation learning paradigm	Learning paradigm
Основополагающий источник	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	incremental learning, sequential learning, streaming learning, online machine learning	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	6	3	3
Сводка	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.