Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Semi-superviseret CatBoost× | Semi-supervised Random Forest× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2018 (CatBoost); semi-supervised learning framework predates 2006 | 2009 |
| Ophavsperson≠ | Prokhorenkova et al. (CatBoost); semi-supervised paradigm from Chapelle et al. | Leistner, C., Saffari, A., Santner, J., & Bischof, H. |
| Type≠ | Semi-supervised ensemble (gradient boosting) | Semi-supervised ensemble classifier |
| Oprindelig kilde≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (NeurIPS), 31. 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 ↗ |
| Aliasser | SSL CatBoost, semi-supervised gradient boosting with CatBoost, CatBoost with unlabeled data, pseudo-label CatBoost | SSL-RF, semi-supervised forest, label-propagation random forest, self-training random forest |
| Relaterede≠ | 5 | 3 |
| Resumé≠ | Semi-supervised CatBoost applies CatBoost's ordered gradient boosting framework to settings where only a fraction of training instances carry labels, leveraging unlabeled data through pseudo-labeling or consistency-based strategies to improve model accuracy beyond what labeled data alone would allow. | 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. |
| ScholarGateDatasæt ↗ |
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