Deep Gaussian mixture models

Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, deep Gaussian mixture models (DGMM) are introduced and discussed. A DGMM is a network of multiple layers of latent variables, where...

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Veröffentlicht in:	Statistics and computing Jg. 29; H. 1; S. 43 - 51
Hauptverfasser:	Viroli, Cinzia, McLachlan, Geoffrey J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2019 Springer Nature B.V
Schlagworte:	Analyzers Artificial Intelligence Machine learning Mathematics and Statistics Probabilistic models Probability and Statistics in Computer Science Regression analysis Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Mixtures of factor analyzers Stochastic EM algorithm Unsupervised classification
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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