Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Aprendizaje semi-supervisado de conjuntos× | Potenciación× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 1998–2005 | 1990–1997 |
| Autor original≠ | Blum & Mitchell (co-training); Zhou & Li (tri-training) | Schapire, R. E.; Freund, Y. |
| Tipo≠ | Ensemble + semi-supervised hybrid paradigm | Sequential ensemble (iterative reweighting) |
| Fuente seminal≠ | Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541. 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 ↗ |
| Alias | semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Relacionados | 6 | 6 |
| Resumen≠ | Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels. | 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. |
| ScholarGateConjunto de datos ↗ |
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