Comparar métodos

	Máquina de Vetores de Suporte em Ensemble	Comitê de Votação
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000–2003	1990s–2004
Autor original	Kim, H.-C. et al.; Dietterich, T. G.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Tipo	Ensemble of SVMs (bagging, voting, or stacking)	Ensemble (combination of multiple classifiers by vote)
Fonte 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.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Outros nomes	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Relacionados	5	5
Resumo	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.	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.