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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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Active Learning with Gaussian Mixture Model

Rekod kaedah taksonomik · ml-model / machine-learning

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