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Active Learning Gaussian Mixture Model

Active Learning Gaussian Mixture Model combines an iterative query strategy with a Gaussian Mixture Model learner. The algorithm selects the most informative unlabeled points — typically those with highest predictive uncertainty — presents them to an oracle for labeling, and refits the GMM using EM on the growing labeled set. The result is a density model that matches full-data quality while requiring far fewer labeled examples.

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Sources

  1. Zhu, X., Ghahramani, Z., & Lafferty, J. (2003). Semi-supervised learning using Gaussian fields and harmonic functions. Proceedings of the 20th International Conference on Machine Learning (ICML), 912–919. link ↗
  2. Settles, B. (2012). Active Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning, 6(1), 1–114. Morgan & Claypool Publishers. DOI: 10.2200/S00429ED1V01Y201207AIM018 ↗

Related methods

Active learning Gaussian processBayesian Gaussian Mixture ModelSemi-supervised Gaussian Mixture ModelSemi-supervised Learning

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ScholarGate — Active learning Gaussian mixture model (Active Learning with Gaussian Mixture Model). Retrieved 2026-06-04 from https://scholargate.app/tr/machine-learning/active-learning-gaussian-mixture-model