Salīdzināt metodes

	Tiešsaistes daļēji uzraudzītā apguve	Pārneses apmācība
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2000s–2010s	2010 (formalized); 1990s (early roots)
Autors	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tips	Incremental / stream-based semi-supervised learning framework	Learning paradigm
Pirmavots	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.
Citi nosaukumi	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Saistītās	6	3
Kopsavilkums	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.