Сравнение методов

	Ансамбль опорных векторов (Ensemble Support Vector Machine)	Бустинг	Случайный лес
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2000–2003	1990–1997	2001
Автор метода	Kim, H.-C. et al.; Dietterich, T. G.	Schapire, R. E.; Freund, Y.	Breiman, L.
Тип	Ensemble of SVMs (bagging, voting, or stacking)	Sequential ensemble (iterative reweighting)	Ensemble (bagging of decision trees)
Основополагающий источник	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.	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	6	4
Сводка	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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.