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...
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| Format: | Article |
| Language: | English |
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Elsevier
2011-11-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866511000363 |