Linganisha mbinu

	Kujifunza kwa Njia Tendaji kwa Kuunganisha Mbinu	Kuimarisha
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1992–2012	1990–1997
Mwanzilishi	Wolpert, D. H. (stacking); Settles, B. (active learning survey)	Schapire, R. E.; Freund, Y.
Aina	Hybrid (active learning + stacked ensemble)	Sequential ensemble (iterative reweighting)
Chanzo asilia	Wolpert, D. H. (1992). Stacked generalization. Neural Networks, 5(2), 241–259.	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	AL-stacking, query-by-committee stacking, active stacked generalization, stacking with active query	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Zinazohusiana	5	6
Muhtasari	Active Learning Stacking Ensemble combines an active learning query loop with stacked generalization: a pool of unlabeled data is available, and the model iteratively selects the most informative instances for human labeling, using those labels to train and refine a stacking ensemble of multiple base learners topped by a meta-learner. This approach reduces annotation cost while maximizing the predictive power of the ensemble.	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.