Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

Unsupervised learning of mixture models based on swarm intelligence and neural networks with optimal completion using incomplete data

In this paper, a new algorithm is presented for unsupervised learning of finite mixture models (FMMs) using data set with missing values. This algorithm overcomes the local optima problem of the Expectation-Maximization (EM) algorithm via integrating the EM algorithm with Particle Swarm Optimization...

Full description

Bibliographic Details
Main Author: Ahmed R. Abas
Format: Article
Language:English
Published: Elsevier 2012-07-01
Series:Egyptian Informatics Journal
Subjects:
Particle Swarm Optimization
Optimal Completion Strategy
Expectation–Maximization
Locally-tuned general regression neural networks
Finite mixture models
Unsupervised learning
Clustering
Incomplete data
Online Access:http://www.sciencedirect.com/science/article/pii/S1110866512000163

Internet

http://www.sciencedirect.com/science/article/pii/S1110866512000163

Similar Items