Linganisha mbinu

	Kupatanisha Kwa Kupiga Kura Kulisaidika kwa Nusu	Kuimarisha
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1998–2005	1990–1997
Mwanzilishi	Zhou, Z.-H. & Li, M. (tri-training); Blum & Mitchell (co-training)	Schapire, R. E.; Freund, Y.
Aina	Semi-supervised ensemble (voting)	Sequential ensemble (iterative reweighting)
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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Majina mbadala	semi-supervised majority vote, SSL voting ensemble, co-training voting classifier, semi-supervised multi-classifier voting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Zinazohusiana	5	6
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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.