GTM: A principled alternative to the Self-Organizing Map

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1112 LNCS, Issue , 1996, Pages 165-170

  Bishop, Christopher M ^a   Svensdn, Markus ^a   Williams, Christopher K I ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CONFORMAL MAPPING; MAPS; NEURAL NETWORKS; LINEAR TRANSFORMATIONS; MATHEMATICAL TRANSFORMATIONS; MAXIMUM PRINCIPLE; PROBABILITY DENSITY FUNCTION; SELF ORGANIZING MAPS;

DATA SPACE; EXPECTATION - MAXIMIZATIONS; FLOW DIAGNOSTICS; GENERATIVE TOPOGRAPHIC MAP; LATENT VARIABLE MODELING; NON-LINEAR TRANSFORMATIONS; OIL PIPELINES; PROBABILITY DENSITIES;

ALGORITHMS;

EID: 84902161562     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-61510-5_31     Document Type: Conference Paper

References (5)

1. Bishop, C. M. and G. D. James (1993). Analysis of multiphase flows using duaienergy gamma densitometry and neural networks. Nuclear Instruments and Methods in Physics Research A 327, 580-593.
2. Dempster, A. P., N. M. Laird, and D. B. Rubin (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Star. Soc B 39 (1), 1-38.
3. Erwin, E., K. Obermayer, and K. Schulten (1992). Self-organizing maps: Ordering, convergence properties and energy functions. Biological Cybernetics 67, 47-55.
4. Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59-69.
5. Kohonen, T. (1995) Sell-Organizing Maps. Berlin: Springer-Verlag.