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	एन्सेम्बल सपोर्ट वेक्टर मशीन (Ensemble Support Vector Machine)	बैगिंग (बूटस्ट्रैप एग्रीगेटिंग)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2000–2003	1996
प्रवर्तक	Kim, H.-C. et al.; Dietterich, T. G.	Breiman, L.
प्रकार	Ensemble of SVMs (bagging, voting, or stacking)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
मौलिक स्रोत	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.
उपनाम	Ensemble SVM, SVM ensemble, bagged SVM, SVM committee machine	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
संबंधित	5	5
सारांश	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.