The weighted em algorithm and block monitoring

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn 3, Issue , 1997, Pages 1936-1941

  Matsuyama, Y ^a,b  

^a WASEDA UNIVERSITY   (Japan)

^b RWCP ^*  (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BUILDING BLOCKES; EM ALGORITHMS; LEARNING STRUCTURE; MAXIMIZATION ALGORITHM; MEASURE OF INFORMATION; PROBABILITY MEASURES; STATISTICAL PROPERTIES; TRADITIONAL LEARNING;

NEURAL NETWORKS; LEARNING ALGORITHMS; PROBABILITY; STATISTICAL METHODS;

ALGORITHMS; LEARNING SYSTEMS;

CRAMER RAO INEQUALITY; EXPECTATION AND MAXIMIZATION (EM) ALGORITHM;

EID: 0030677958     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICNN.1997.614195     Document Type: Conference Paper

References (12)

1. A.P. Dempster, N.M. Laird and D.B. Rubin, Maxi-mum likelihood from incomplete data via the EM algorithm, J. R. Statist. Society, Series B, Vol. 39, pp. 1-38, 1978.
2. M.J. Jordan and R.A. Jacobs, Hierarchical mixtures of experts and the EM algorithm, Neural Computation, Vol. 6, pp. 181-214, 1994.
3. S. Amari, Information geometry of the EM and em algorithms for neural nets, Neural Networks, Vol. 8, pp. 1379-1408, 1995.
4. S. Amari, Information geometry of neural networks-New Bayesian duality theory-, Proc. ICONIP'96, Vol 1, pp. 3-6, September, 1996.
5. L. Xu, Bayesian-Kullback Ying-Yang Machine: Reviews and new results, Proc. ICONIP'96, Vol. 1, pp. 59-67, September, 1996.
6. A. Renyi, On measures of entropy and information, Proc. 4th Berkeley Symp. Math. Stat, and Pr., Vol. 1, pp. 547-561, 1960.
7. J.H. Havrda and F. Chavat, Qualification methods of classification processes: Concepts of structural a entropy, Kybernetica, Vol. 3, pp. 30-35, 1967.
8. S. Amari and H. Nagaoka, Methods of Information Geometry, Iwanami Publishing Co., Tokyo, 1993.
9. I. Csiszar, A class of measure of informativity of observation channels, Periodica Mathematica Hun-garica, Vol. 2, pp. 191-213, 1972.
10. Y. Matsuyama, The a-EM algorithm and its block connections, Tech. Report, No. CML-96-10, Computation Mechanism Lab., Dept. of EECE, Waseda Univ., October, 1996.
11. Y. Matsuyama, The weighted EM learning and monitoring structure, Info. Proc. Soc. of Japan, 54-th Convention Record, 6G-04, March, 1997.
12. Y. Matsuyama, The a-EM algorithm: A block connectable generalized learning tool for neural networks, Proc. IWANN, June, 1097.