So sánh phương pháp

	Tăng cường Gradient bán giám sát	Học tăng cường tự giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2006–2010s	2018–2020
Người khởi xướng	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	LeCun, Y. and community (formalized ~2018–2020)
Loại	Semi-supervised ensemble (self-training + gradient boosted trees)	Representation learning paradigm
Công trình gốc	Yarowsky, D. (1995). Unsupervised word sense disambiguation rivaling supervised methods. Proceedings of ACL 1995, 189–196. (Foundational self-training framework underlying pseudo-label approaches.)	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Tên gọi khác	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	6	3
Tóm tắt	Semi-supervised gradient boosting combines gradient boosted trees with self-training or pseudo-labeling to exploit large pools of unlabeled data alongside a small labeled set. An initial GBM fit on labeled data assigns confident predictions to unlabeled examples; those pseudo-labeled points are folded back into training and the model is re-boosted, iterating until convergence. This allows practitioners to harness cheap unlabeled data when labels are scarce or expensive.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.