手法を比較

	半教師あり勾配ブースティング	自己教師あり学習
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2006–2010s	2018–2020
提唱者	Chapelle, Scholkopf & Zien (eds.); applied to GBM variants in subsequent literature	LeCun, Y. and community (formalized ~2018–2020)
種類	Semi-supervised ensemble (self-training + gradient boosted trees)	Representation learning paradigm
原典	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/
別名	pseudo-label gradient boosting, self-training GBM, semi-supervised GBT, label-propagation boosting	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
関連	6	3
概要	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.