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| Wzmocnienie× | Wzmocnienie półnadzorowane× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1990–1997 | 1999–2009 |
| Twórca≠ | Schapire, R. E.; Freund, Y. | Mallapragada, P. K.; Bennett, K. P.; and others |
| Typ≠ | Sequential ensemble (iterative reweighting) | Semi-supervised ensemble method |
| Źródło pierwotne≠ | 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 ↗ | 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 ↗ |
| Inne nazwy | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | SemiBoost, SSL boosting, boosting with unlabeled data, semi-supervised ensemble boosting |
| Pokrewne≠ | 6 | 5 |
| Podsumowanie≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
| ScholarGateZbiór danych ↗ |
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