Linganisha mbinu

	Kupatanisha Kwa Kupiga Kura Kulisaidika kwa Nusu	Jifunze kwa Kujisimamia
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1998–2005	2018–2020
Mwanzilishi	Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)	LeCun, Y. and community (formalized ~2018–2020)
Aina	Semi-supervised ensemble (voting)	Representation learning paradigm
Chanzo asilia	Zhou, Z.-H., & Li, M. (2005). Tri-training: Exploiting unlabeled data using three classifiers. IEEE Transactions on Knowledge and Data Engineering, 17(11), 1529–1541.	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/
Majina mbadala	semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Zinazohusiana	5	3
Muhtasari	A semi-supervised voting ensemble trains multiple classifiers on a small labeled set, then iteratively exploits unlabeled data by having the classifiers label examples they agree on, expanding the training pool until all classifiers vote jointly on test examples. It combines the label-efficiency of semi-supervised learning with the variance-reduction of majority-vote ensembles, making it valuable when annotation is costly.	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.