Using incremental general regression neural network for learning mixture models from incomplete data

Using incremental general regression neural network for learning mixture models from incomplete data

Finite mixture models (FMM) is a well-known pattern recognition method, in which parameters are commonly determined from complete data using the Expectation Maximization (EM) algorithm. In this paper, a new algorithm is proposed to determine FMM parameters from incomplete data. Compared with a modif...

Full description

Bibliographic Details
Main Author: Ahmed R. Abas
Format: Article
Language:English
Published: Elsevier 2011-11-01
Series:Egyptian Informatics Journal
Subjects:
Cluster analysis
Expectation maximization
Finite mixture models
Incomplete data
Incremental general regression neural network
Local correlations
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866511000363

Internet

http://www.sciencedirect.com/science/article/pii/S1110866511000363

Similar Items