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Ensemble Gaussian Mixture Model

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.

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Ensemble Gaussian Mixture Model (E-GMM)

Taksonomisk metoderegister · ml-model / machine-learning

Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 9: Mixture Models and EM). Springer. · ISBN 978-0-387-31073-2
Dietterich, T. G. (2000). Ensemble methods in machine learning. Multiple Classifier Systems, Lecture Notes in Computer Science, 1857, 1–15. · DOI 10.1007/3-540-45014-9_1

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