Sammenlign metoder

	Selv-overvåget beslutningstræ	Label Propagation
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2015–present	2002
Ophavsperson	Multiple authors (active research area, 2010s–2020s)	Zhu, X. & Ghahramani, Z.
Type	Self-supervised ensemble/single tree model	Graph-based semi-supervised classification
Oprindelig kilde	Self-supervised learning. Wikipedia.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Aliasser	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Relaterede	5	3
Resumé	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.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.