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...

Celý popis

Podrobná bibliografia

Vydané v:	Statistics and computing Ročník 29; číslo 1; s. 43 - 51
Hlavní autori:	Viroli, Cinzia, McLachlan, Geoffrey J.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.01.2019 Springer Nature B.V
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!