Compara mètodes

	màquina de vectors de suport d'ensemble	Bagging (Bootstrap Aggregating)	Stacking
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2000–2003	1996	1992
Autor original	Kim, H.-C. et al.; Dietterich, T. G.	Breiman, L.	Wolpert, D.H.
Tipus	Ensemble of SVMs (bagging, voting, or stacking)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (heterogeneous meta-learning)
Font seminal	Kim, H.-C., Pang, S., Je, H.-M., Kim, D., & Bang, S. Y. (2002). Constructing support vector machine ensemble. Pattern Recognition, 36(12), 2757–2767.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Wolpert, D.H. (1992). Stacked Generalization. Neural Networks, 5(2), 241–259.
Àlies	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Stacking (Yığınlama — Meta-Öğrenme), stacked generalization, meta-learning ensemble, super learner
Relacionats	5	5	5
Resum	Ensemble Support Vector Machine combines multiple independently trained SVM classifiers or regressors — each fitted on a different data partition, bootstrap sample, or feature subset — and aggregates their outputs via voting, averaging, or stacking. The approach mitigates the high computational cost and sensitivity to kernel hyperparameters inherent in a single large-scale SVM, while improving generalisation on complex or high-dimensional datasets.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Stacking, or stacked generalization, is an ensemble method introduced by David Wolpert in 1992 that combines the outputs of several different base models (Level-0) through a separate meta-model (Level-1). Unlike bagging and boosting, it deliberately uses heterogeneous model types, and it is the standard final-stage strategy in Kaggle competitions.