Krahasoni metodat

	Gradient Boosting vetë-i-mbikëqyrur	XGBoost
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2020s	2016
Krijuesi	Various researchers (Zhang et al. and others)	Chen, T. & Guestrin, C.
Lloji	Ensemble (self-supervised + gradient boosting)	Ensemble (gradient-boosted decision trees)
Burimi themelues	Zhang, Y., Zhang, J., & Yang, Q. (2022). Self-Supervised Gradient Boosting for Semi-Supervised Learning on Tabular Data. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Emërtime të tjera	SSL gradient boosting, self-supervised boosting, semi-supervised gradient boosting, SSL-GBM	XGBoost, extreme gradient boosting, scalable tree boosting
Të lidhura	5	5
Përmbledhja	Self-supervised gradient boosting extends the classic gradient boosting framework by incorporating self-supervised pretext tasks to exploit unlabeled data. The model first learns useful feature representations from unannotated samples, then uses those representations to guide the sequential ensemble of weak learners, achieving strong predictive performance even when labeled examples 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.