Number of Components and Initialization in Gaussian Mixture Model for Pattern Recognition-Reference-Cited by-同舟云学术

Number of Components and Initialization in Gaussian Mixture Model for Pattern Recognition

Published:2001 Issue: Volume: Page:406-409
ISSN:
Container-title:Artificial Neural Nets and Genetic Algorithms
language:
Short-container-title:

Author:

Paclík Pavel,Novovičová Jana

Publisher

Springer Vienna

Link

http://link.springer.com/content/pdf/10.1007/978-3-7091-6230-9_101

Reference11 articles.

1. G. Celeux and G. Soromenho. An entropy criterion for assessing the number of clusters in a mixture model. Journal of Classification, 13: 195–212, 1996.

2. S. V. Chakravarthy and J. Ghosh. Scale-Based Clustering Using the Radial Basis Function Network. IEEE Trans. on Neural Networks, 7(5):1250–1261, 1996.

3. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via EM algorithm. J. Royal Stat.Soc. vol.39, pp. 1–38, 1977.

4. J. Grim, J. Novovicova, P. Pudil, P. Somol, and F. Ferri. Initialization normal mixtures of densities. In Proceedings of the 14th ICPR, pages 886–890, Australia, 1998.

5. N. Kehtamavaz and E. Nakamura. Generalization of the EM algorithm for mixture density estimation. Pattern Recognition Letters, 19:133–140, 1998.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Mixture Initialization Based on Prior Data Visual Analysis;Studies in Computational Intelligence;2018-06-28

2. Anti-Models:;Text, Speech and Dialogue;2014

3. Estimation of Single-Gaussian and Gaussian Mixture Models for Pattern Recognition;Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications;2013

4. Covariance Matrix Enhancement Approach to Train Robust Gaussian Mixture Models of Speech Data;Speech and Computer;2013