Using general regression with local tuning for learning mixture models from incomplete data sets

Using general regression with local tuning for learning mixture models from incomplete data sets

Finite mixture models is a pattern recognition technique that is used for fitting complex data distributions. Parameters of this mixture models are usually determined via the Expectation Maximization (EM) algorithm. A modified version of the EM algorithm is proposed earlier to handle data sets with...

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Bibliographic Details
Main Author: Ahmed R. Abas
Format: Article
Language:English
Published: Elsevier 2010-12-01
Series:Egyptian Informatics Journal
Subjects:
Finite mixture models
Expectation Maximization
Missing value
Incomplete data set
Locally tuned general regression
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866510000204

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http://www.sciencedirect.com/science/article/pii/S1110866510000204

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