So sánh phương pháp

	Rừng Ngẫu nhiên Tự giám sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2012–2022	1970s–2006 (formalized)
Người khởi xướng	Lefortier, D. et al.; Criminisi, A. et al. (semi-supervised RF lineage)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Semi-supervised ensemble (self-supervised pretext task + RF)	Learning paradigm
Công trình gốc	Lefortier, D., Chitta, K., & Agrawal, P. (2022). Self-supervised random forests. arXiv:2204.01430.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Tên gọi khác	SSL-RF, self-supervised RF, self-supervised ensemble forest, unsupervised random forest with self-labeling	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	6	5
Tóm tắt	Self-supervised Random Forest (SSL-RF) extends the classic random forest to settings where labeled examples are scarce. The forest is first trained using automatically generated pseudo-labels derived from a self-supervised pretext task — such as predicting data transformations or masked features — and then refined on whatever true labels are available, marrying the label-efficiency of self-supervised learning with the robustness of ensemble trees.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.