Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Online Random Forest× | Случайный лес с частичной разметкой× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления | 2009 | 2009 |
| Автор метода≠ | Saffari, A. et al. | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| Тип≠ | Incremental ensemble (streaming decision trees) | Semi-supervised ensemble classifier |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | ORF, streaming random forest, incremental random forest, adaptive random forest | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| Связанные≠ | 6 | 3 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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