Self-organizing mixture networks for probability density estimation

IEEE Transactions on Neural Networks

Volumn 12, Issue 2, 2001, Pages 405-411

  Yin, H ^a   Allinson, N M ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Density estimation; Expectation maximization (EM) algorithm; Maximum likelihood; Mixture distribution; Self organizing maps; Unsupervised learning

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; LEARNING SYSTEMS; PATTERN RECOGNITION; PROBABILITY DENSITY FUNCTION; RANDOM PROCESSES;

EXPECTATION-MAXIMIZATION (EM) ALGORITHMS; SELF-ORGANIZING MIXTURE NETWORKS (SOMN);

SELF ORGANIZING MAPS;

EID: 0035272084     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/72.914534     Document Type: Article

References (23)

1. Unsupervised learning by EM algorithm based on finite mixture of Gaussians
2. Maximum likelihood from incomplete date via the EM algorithm
3. Mixture densities, maximum likelihood and the EM algorithm
4. Averaging, maximum penalised likelihood and Bayesian estimation for improving Gaussian mixture probability density estimates
5. Comparison of a Bayesian SOM with the EM algorithm for Gaussian mixtures
6. Bayesian learning for self-organizing maps
7. Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach
8. Hyperparameter selection for self-organizing maps
9. GTM: The generative topographic mapping
10. An analysis of the elastic net approach to the travelling salesman problem
11. On information and sufficiency
12. A stochastic approximation method
13. Self-organized formation of topologically correct feature maps
14. Adaptive mixtures of local experts
15. On convergence properties of the EM algorithm for Gaussian mixtures
16. Convergence results for the EM approach to mixture of experts architectures
17. Competitive and self-organizing algorithms based on the minimization of an information criterion
18. Learning in artificial neural networks
19. A Bayesian self-organizing map for Gaussian mixtures
20. Dynamic analysis of capillary electrophoresis data using real-time neural networks