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	एन्सेम्बल गॉसियन मिक्सचर मॉडल	बैगिंग (बूटस्ट्रैप एग्रीगेटिंग)
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	2000s	1996
प्रवर्तक	Combination of GMM (Dempster et al., 1977) and ensemble learning (Dietterich, 2000)	Breiman, L.
प्रकार	Ensemble of probabilistic generative models	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
मौलिक स्रोत	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9: Mixture Models and EM). Springer. ISBN: 978-0-387-31073-2	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
उपनाम	E-GMM, GMM ensemble, mixture model ensemble, ensemble GMM	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
संबंधित	4	5
सारांश	Ensemble Gaussian Mixture Model (E-GMM) combines multiple independently fitted Gaussian Mixture Models to improve density estimation, clustering stability, and anomaly detection. By averaging or aggregating the probabilistic outputs of several GMMs — each trained on a different data subset or random initialization — the ensemble reduces sensitivity to local optima and random seed choice, yielding more robust and reliable results than any single GMM.	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.