Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Ensemble semi-superviseret læring× | Boosting× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 1998–2005 | 1990–1997 |
| Ophavsperson≠ | Blum & Mitchell (co-training); Zhou & Li (tri-training) | Schapire, R. E.; Freund, Y. |
| Type≠ | Ensemble + semi-supervised hybrid paradigm | Sequential ensemble (iterative reweighting) |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. |
| ScholarGateDatasæt ↗ |
|
|