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...
Egile Nagusiak: | Gámez Martín, José Antonio, Rumí, Rafael, Salmerón Cerdán, Antonio |
---|---|
Formatua: | info:eu-repo/semantics/report |
Hizkuntza: | English |
Argitaratua: |
2012
|
Sarrera elektronikoa: | http://hdl.handle.net/10835/1555 |
Antzeko izenburuak
-
Selective naive Bayes predictor with mixtures of truncated exponentials
nork: Morales, María, et al.
Argitaratua: (2012) -
Learning naive Bayes regression models with missing data using mixtures of truncated exponentials
nork: Fernández, Antonio, et al.
Argitaratua: (2012) -
Estimating mixtures of truncated exponentials from data
nork: Moral, Serafín, et al.
Argitaratua: (2012) -
Approximate Probability Propagation with Mixtures of Truncated Exponentials*
nork: Rumí, Rafael, et al.
Argitaratua: (2017) -
Parameter Estimation in Mixtures of Truncated Exponentials
nork: Langseth, Helge, et al.
Argitaratua: (2012)