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| Többségi szavazás× | AdaBoost× | |
|---|---|---|
| Tudományterület≠ | Együttes tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1996 | 1997 |
| Megalkotó≠ | Leo Breiman | Freund, Y. & Schapire, R.E. |
| Típus≠ | voting aggregation | Ensemble (sequential boosting of weak learners) |
| Alapmű≠ | Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. 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≠ | hard voting | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma |
| Kapcsolódó | 5 | 5 |
| Összefoglaló≠ | Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy. | 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. |
| ScholarGateAdatkészlet ↗ |
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