A relaxation algorithm influenced by self-organizing maps

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

Volumn 2714, Issue , 2003, Pages 546-553

  Maeda, Michiharu ^a  

^a KURUME NATIONAL COLLEGE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CONFORMAL MAPPING; IMAGE RECONSTRUCTION; NEURAL NETWORKS; PIXELS; RESTORATION;

NOVEL ALGORITHM; ORDERING PROCESS; ORIGINAL IMAGES; PIXEL VALUES; RELAXATION ALGORITHM; RESTORATION PROCESS; WEIGHT VECTOR;

SELF ORGANIZING MAPS;

EID: 21144433477     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44989-2_65     Document Type: Article

References (17)

1. Grossberg, S.: Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biol. Cybern. 23 (1976) 121-134
2. Willshaw, D.J., Malsburg, C.: How patterned neural connections can be set up by self-organization. Proc. R. Soc. Lond. B. 194 (1976) 431-445
3. Hertz, J., Krogh, A., Palmer, R.G.: Introduction to the theory of neural computation. Addison-Wesley (1991)
4. Kohonen, T.: Self-organizing maps. Springer-Verlag Berlin (1995)
5. Bauer, H.-U., Pawelzik, K.R.: Quantifying the neighborhood preservation of self-organizing feature maps. IEEE Trans. Neural Networks 3 (1992) 570-579
6. Martinetz, T., Schulten, K.: Topology representing networks. Neural Networks 7 (1994) 507-522
7. Villmann, T., Herrmann, M., Martinetz, T.M.: Topology preservation in self-organizing feature maps: Exact definition and measurement. IEEE Trans. Neural Networks 8 (1997) 256-266
8. Durbin, R., Willshaw, D.: An analogue approach to the traveling salesman problem using an elastic net method. Nature 326 (1987) 689-691
9. Angéniol, B., Vaubois, G. de La C., Le Texier, J.-Y.: Self-organizing feature maps and the traveling salesman problem. Neural Networks 1 (1988) 289-293
10. Ritter, H., Schulten, K.: On the stationary state of Kohonen's self-organizing sensory mapping. Biol. Cybern. 54 (1986) 99-106
11. Ritter, H., Schulten, K.: Convergence properties of Kohonen's topology conserving maps, Fluctuations, stability, and dimension selection. Biol. Cybern. 60 (1988) 59-71
12. Tanaka, T., Saito, M.: Quantitative properties of Kohonen's self-organizing maps as adaptive vector quantizers. IEICE Trans. J75-D-II (1992) 1085-1092
13. Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: "Neural-gas" network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Networks 4 (1993) 558-569
14. Maeda, M., Miyajima, H., Murashima, S.: An adaptive learning and self-deleting neural network for vector quantization. IEICE Trans. Fundamentals E79-A (1996) 886-1893
15. Maeda, M., Miyajima, H.: Competitive learning methods with refractory and creative approaches. IEICE Trans. Fundamentals E82-A (1999) 1825-1833
16. Geman S., Geman D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intel. 6 (1984) 721-741
17. Maeda, M., Ohta, M., Miyajima, H.: An algorithm of statistical mechanics for image restoration. Proc. IEICE Int. Symp. Nonlinear Theory and Its Applications 1 (2001) 107-110