Machine learningMachine learning
半监督随机森林
半监督随机森林 (SSL-RF) 通过利用有标签和无标签的训练样本来扩展经典的随机森林。当标注数据成本高昂或耗时时，SSL-RF 通过森林本身为无标签观测值分配暂定的伪标签，然后对丰富后的数据集进行重新训练，在无需额外人工标注的情况下逐步提高准确性。
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来源
- 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: 10.1109/ICCV.2009.5459198 ↗
- Zhu, X. (2005). Semi-supervised learning literature survey. Computer Sciences Technical Report 1530, University of Wisconsin-Madison. link ↗
如何引用本页
ScholarGate. (2026, June 3). Semi-supervised Random Forest (SSL-RF). ScholarGate. https://scholargate.app/zh/machine-learning/semi-supervised-random-forest
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