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| Wzmocnienie półnadzorowane× | AdaBoost× | Uczenie ze wsparciem częściowym× | |
|---|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning | Machine learning |
| Rok powstania≠ | 1999–2009 | 1997 | 1970s–2006 (formalized) |
| Twórca≠ | Mallapragada, P. K.; Bennett, K. P.; and others | Freund, Y. & Schapire, R.E. | Vapnik, V. N. and others (community of researchers, 1970s–2000s) |
| Typ≠ | Semi-supervised ensemble method | Ensemble (sequential boosting of weak learners) | Learning paradigm |
| Źródło pierwotne≠ | 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 ↗ | Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9 |
| Inne nazwy≠ | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | SSL, semi-supervised machine learning, transductive learning, label-efficient learning |
| Pokrewne | 5 | 5 | 5 |
| Podsumowanie≠ | 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. | 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. |
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