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| Félfelügyelt Boosting× | AdaBoost× | |
|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1999–2009 | 1997 |
| Megalkotó≠ | Mallapragada, P. K.; Bennett, K. P.; and others | Freund, Y. & Schapire, R.E. |
| Típus≠ | Semi-supervised ensemble method | Ensemble (sequential boosting of weak learners) |
| Alapmű≠ | 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. DOI ↗ | 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. DOI ↗ |
| Alternatív nevek≠ | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | 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. |
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