Сравнение методов

	Онлайновое полуавтоматическое обучение	Перенос обучения
Область	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning
Год появления	2000s–2010s	2010 (formalized); 1990s (early roots)
Автор метода	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Тип	Incremental / stream-based semi-supervised learning framework	Learning paradigm
Основополагающий источник	Goldberg, A., Li, M., & Zhu, X. (2008). Online manifold regularization: A new learning setting and empirical study. In Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), pp. 393–407. Springer.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Другие названия	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Связанные	6	3
Сводка	Online semi-supervised learning combines the incremental, one-pass nature of online learning with the ability to exploit unlabeled data alongside sparse labeled observations. It is designed for settings where data arrives as a stream and obtaining labels for every instance is expensive or impractical — such as real-time classification of web content, sensor readings, or social media posts.	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.