قارن الطرق

	الانحدار اللوجستي ذاتي الإشراف	شجرة القرار ذاتية الإشراف
المجال	تعلم الآلة	تعلم الآلة
العائلة	Machine learning	Machine learning
سنة النشأة	2020s	2015–present
صاحب الطريقة	Chen et al. (SimCLR linear evaluation protocol, 2020); logistic probe practice widely adopted across SSL literature	Multiple authors (active research area, 2010s–2020s)
النوع	Self-supervised pretraining + supervised linear classification	Self-supervised ensemble/single tree model
المصدر التأسيسي	Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A Simple Framework for Contrastive Learning of Visual Representations. Proceedings of the 37th International Conference on Machine Learning (ICML), 1597–1607.	Self-supervised learning. Wikipedia.
الأسماء البديلة	SSL linear probe, contrastive pretraining with logistic classifier, self-supervised linear evaluation, SSL + logistic regression	SSL decision tree, self-supervised tree classifier, pseudo-label decision tree, unsupervised-guided decision tree
ذات صلة	5	5
الملخص	Self-supervised logistic regression is a two-stage pipeline in which a neural encoder is first trained on abundant unlabeled data through a self-supervised pretext task — such as contrastive learning or masked prediction — and then the frozen learned representations are classified with a standard logistic regression model trained on a small labeled dataset. This linear evaluation protocol is widely used to benchmark the quality of self-supervised representations.	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.