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Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| [UNTRANSLATED: AdaBoost]× | Label Propagation× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1997 | 2002 |
| Ideatore≠ | Freund, Y. & Schapire, R.E. | Zhu, X. & Ghahramani, Z. |
| Tipo≠ | Ensemble (sequential boosting of weak learners) | Graph-based semi-supervised classification |
| Fonte seminale≠ | 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 ↗ | Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University. link ↗ |
| Alias≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | LP, label spreading, graph-based semi-supervised learning, harmonic label propagation |
| Correlati≠ | 5 | 3 |
| Sintesi≠ | 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. | Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data. |
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