Linganisha mbinu

	Robust Bagging	Kikundi cha Kura (Voting Ensemble)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1996–2000s	1990s–2004
Mwanzilishi	Breiman, L. (bagging); robust variants developed by various authors in 2000s	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Aina	Ensemble (robust bootstrap aggregating)	Ensemble (combination of multiple classifiers by vote)
Chanzo asilia	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123–140.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Majina mbadala	robust bootstrap aggregating, robust ensemble bagging, outlier-resistant bagging, robust BAGGing	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Zinazohusiana	6	5
Muhtasari	Robust Bagging extends the classic Bootstrap Aggregating (Bagging) framework by replacing or augmenting standard base learners with robust estimators — or by using robust aggregation rules — so that the ensemble remains accurate even when training data contain outliers, mislabelled instances, or heavy-tailed noise distributions.	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.