방법 비교

	준지도 학습 부스팅	레이블 전파	준지도 학습
분야	머신러닝	머신러닝	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1999–2009	2002	1970s–2006 (formalized)
창시자	Mallapragada, P. K.; Bennett, K. P.; and others	Zhu, X. & Ghahramani, Z.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
유형	Semi-supervised ensemble method	Graph-based semi-supervised classification	Learning paradigm
원전	Mallapragada, P. K., Jin, R., Jain, A. K., & Liu, Y. (2009). SemiBoost: Boosting for Semi-supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), 2000–2014.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
별칭	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
관련	5	3	5
요약	Semi-supervised Boosting is an ensemble learning paradigm that extends classical boosting algorithms — such as AdaBoost — to exploit both labeled and unlabeled data. By propagating label information through a similarity structure over unlabeled instances, it trains stronger classifiers than supervised boosting alone when labeled data are scarce.	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.