Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ensemble Semi-supervised Learning× | Pastiprināšana× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1998–2005 | 1990–1997 |
| Autors≠ | Blum & Mitchell (co-training); Zhou & Li (tri-training) | Schapire, R. E.; Freund, Y. |
| Tips≠ | Ensemble + semi-supervised hybrid paradigm | Sequential ensemble (iterative reweighting) |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. |
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