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The More, the Merrier: the Blessing of Dimensionality for Learning Large Gaussian Mixtures
Joseph Anderson
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Oznake: computer science;machine learning;unsupervised learning;statistical learning;supervised learning;on-line learning;computational learning theory
In this paper we show that very large mixtures of Gaussians are efficiently learnable in high dimension. More precisely, we prove that a mixture with known identical covariance matrices whose number of components is a polynomial of any fixed degree in the dimension n is polynomially learnable as lon ...
Leto: 2014 Vir: videolectures.net
Št. zadetkov: 1
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