Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Online Random Forest× | Semi-supervised Random Forest× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan | 2009 | 2009 |
| Grondlegger≠ | Saffari, A. et al. | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| Type≠ | Incremental ensemble (streaming decision trees) | Semi-supervised ensemble classifier |
| Oorspronkelijke bron≠ | Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE. link ↗ | Leistner, C., Saffari, A., Santner, J., & Bischof, H. (2009). Semi-supervised random forests. In Proceedings of the IEEE 12th International Conference on Computer Vision (ICCV), pp. 506–513. IEEE. DOI ↗ |
| Aliassen | ORF, streaming random forest, incremental random forest, adaptive random forest | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| Verwant≠ | 6 | 3 |
| Samenvatting≠ | Online Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time. | Semi-supervised Random Forest (SSL-RF) extends the classic Random Forest by exploiting both labeled and unlabeled training examples. When labeling data is expensive or time-consuming, SSL-RF assigns tentative pseudo-labels to unlabeled observations through the forest itself, then retrains on the enriched dataset, progressively improving accuracy without requiring additional human annotation. |
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