Linganisha mbinu

	Ensemble ya Kujiregularisha kwa Kuunganisha (Regularized Stacking Ensemble)	Kuimarisha
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1992–1996	1990–1997
Mwanzilishi	Wolpert, D. H. (stacking); Breiman, L. (regularized meta-learner formulation)	Schapire, R. E.; Freund, Y.
Aina	Ensemble (stacked generalization with regularized meta-learner)	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	regularized stacked generalization, ridge stacking, lasso meta-learner ensemble, penalized stacking	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Zinazohusiana	6	6
Muhtasari	Regularized Stacking Ensemble is a two-level ensemble method in which predictions from multiple diverse base learners are combined by a regularized meta-learner — typically ridge regression, lasso, or elastic net — to suppress overfitting in the combination layer. Regularization ensures that the meta-learner assigns stable, well-calibrated weights to base model outputs rather than memorizing noise in the training fold predictions.	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.