Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

Unsupervised naive Bayes for data clustering with mixtures of truncated exponentials

In this paper we propose a naive Bayes model for unsupervised data clustering, where the class variable is hidden. The feature variables can be discrete or continuous, as the conditional distributions are represented as mixtures of truncated exponentials (MTEs). The number of classes is determined u...

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Bibliografiske detaljer
Main Authors:	Gámez Martín, José Antonio, Rumí, Rafael, Salmerón Cerdán, Antonio
Format:	info:eu-repo/semantics/report
Sprog:	English
Udgivet:	2012
Online adgang:	http://hdl.handle.net/10835/1555

Lignende værker

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Approximate Probability Propagation with Mixtures of Truncated Exponentials*
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Parameter Estimation in Mixtures of Truncated Exponentials
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Udgivet: (2012)

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Linking data

Hábeas data
af: Guerra Pérez, Walter D.

Modelling and Inference with Conditional Gaussian Probabilistic Decision Graphs*
af: Nielsen, Jens D., et al.
Udgivet: (2017)

HORTS 07626 Data
af: Guzmán Palomino, José Miguel, et al.
Udgivet: (2017)