So sánh phương pháp

	Cây quyết định tự giám sát	Cây Quyết định
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2015–present	1984
Người khởi xướng	Multiple authors (active research area, 2010s–2020s)	Breiman, Friedman, Olshen & Stone
Loại	Self-supervised ensemble/single tree model	Recursive partitioning (if-then rules)
Công trình gốc	Self-supervised learning. Wikipedia.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Tên gọi khác	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Liên quan	5	5
Tóm tắt	Self-supervised Decision Tree learning combines the interpretability of classical decision trees with the ability to exploit large quantities of unlabeled data through self-supervised pretext tasks. The model learns useful feature representations or node-split criteria from unlabeled samples before refining predictions on a small labeled set, bridging the gap between fully supervised trees and purely unsupervised clustering.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.