Linganisha mbinu

	Jifunze la Nusu-msaada la Ensemble (Ensemble Semi-supervised Learning)	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1998–2005	1990s–2004
Mwanzilishi	Blum & Mitchell (co-training); Zhou & Li (tri-training)	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble + semi-supervised hybrid paradigm	Ensemble (combination of multiple classifiers by vote)
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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	semi-supervised ensemble, SSL ensemble, ensemble-based SSL, co-training ensemble	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	6	5
Muhtasari	Ensemble semi-supervised learning combines multiple base learners with the semi-supervised paradigm, exploiting both a small labeled set and a large pool of unlabeled data. By letting diverse classifiers teach each other through pseudo-labeling or co-training, the ensemble improves generalization far beyond what either approach alone could achieve with limited labels.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.