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...

書誌詳細
主要な著者:	Gámez Martín, José Antonio, Rumí, Rafael, Salmerón Cerdán, Antonio
フォーマット:	info:eu-repo/semantics/report
言語:	English
出版事項:	2012
オンライン･アクセス:	http://hdl.handle.net/10835/1555

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