手法を比較

	アンサンブルサポートベクターマシン	ブースティング
分野	機械学習	機械学習
系統	Machine learning	Machine learning
提唱年	2000–2003	1990–1997
提唱者	Kim, H.-C. et al.; Dietterich, T. G.	Schapire, R. E.; Freund, Y.
種類	Ensemble of SVMs (bagging, voting, or stacking)	Sequential ensemble (iterative reweighting)
原典	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.
別名	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
関連	5	6
概要	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.