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| Rừng Ngẫu nhiên Trực tuyến× | Random Forest Bán Giám Sát× | |
|---|---|---|
| Lĩnh vực | Học máy | Học máy |
| Họ | Machine learning | Machine learning |
| Năm ra đời | 2009 | 2009 |
| Người khởi xướng≠ | Saffari, A. et al. | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| Loại≠ | Incremental ensemble (streaming decision trees) | Semi-supervised ensemble classifier |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | ORF, streaming random forest, incremental random forest, adaptive random forest | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| Liên quan≠ | 6 | 3 |
| Tóm tắt≠ | 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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