Compara mètodes

	Impulsió auto-supervisada	XGBoost
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2010s–2020s	2016
Autor original	Various researchers (2010s–2020s)	Chen, T. & Guestrin, C.
Tipus	Ensemble (self-supervised + boosting)	Ensemble (gradient-boosted decision trees)
Font seminal	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (pp. 189–196). ACL.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Àlies	SSL boosting, self-supervised ensemble boosting, pretext-task boosting, SSL-Boost	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionats	6	5
Resum	Self-supervised boosting integrates self-supervised pretext tasks into the boosting framework — covering AdaBoost, gradient boosting, and their modern variants — to leverage large pools of unlabeled data. By first learning feature representations from unlabeled samples and then running sequential weak-learner ensembles on pseudo-labeled data, it achieves competitive accuracy even when ground-truth labels are scarce.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.