방법 비교

	준지도 학습 부스팅	AdaBoost
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1999–2009	1997
창시자	Mallapragada, P. K.; Bennett, K. P.; and others	Freund, Y. & Schapire, R.E.
유형	Semi-supervised ensemble method	Ensemble (sequential boosting of weak learners)
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.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.
별칭	SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
관련	5	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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.