השוואת שיטות

	למידה מקוונת מבוססת-למחצה	אלגוריתם התפשטות התוויות (Label Propagation)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2000s–2010s	2002
הוגה השיטה	Goldberg, A., Li, M., & Zhu, X. (and others in stream learning community)	Zhu, X. & Ghahramani, Z.
סוג	Incremental / stream-based semi-supervised learning framework	Graph-based semi-supervised classification
מקור מכונן	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.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
כינויים	stream-based semi-supervised learning, incremental semi-supervised learning, online SSL, semi-supervised online learning	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
קשורות	6	3
תקציר	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.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.