Ensemble of structure-adaptive self-organizing maps for high performance classification

Information sciences

Volumn 123, Issue 1, 2000, Pages 103-114

  Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; DATABASE SYSTEMS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; VECTORS;

STRUCTURE-ADAPTIVE SELF-ORGANIZING MAPS (SOM);

PATTERN RECOGNITION;

EID: 0033900205     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0020-0255(99)00112-7     Document Type: Article

References (19)

1. Cho S.-B., Kim J.H. Combining multiple neural networks by fuzzy integral for robust classification. IEEE Trans. Systems Man Cybernet. 25:1995;380-384.
2. Cho S.-B. Self-organizing map with dynamical node splitting: application to handwritten digit recognition. Neural Comput. 9(6):1997;1343-1353.
3. Cohen E., Hull J.J., Srihari S.N. Understanding handwritten text in a structured environment: determining ZIP codes from addresses. Int. J. Pattern Recognition Art. Intell. 5(1&2):1991;221-264.
4. Fritzke B. Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Networks. 7(9):1994;1441-1460.
5. Y.J. Kim, S.W. Lee, Off-line recognition of unconstrained handwritten digits using multilayer backpropagation neural network combined with genetic algorithm, in: Proceedings of the Sixth Workshop on Image Processing and Understanding, 1994, pp. 186-193.
6. Knerr S., Personnaz L., Dreyfus G. Handwritten digit recognition by neural networks with single-layer training. IEEE Trans. Neural Networks. 3(6):1992;962-968.
7. Kohonen T. The self-organizing map. Proceedings of the IEEE. 78(9):1990;1464-1480.
8. Kohonen T. Self-Organizing Maps. 1995;Springer, Berlin.
9. P. Koikkalainen, E. Oja, Self-organizing hierarchical feature maps, in: Proceedings of the International Joint Conference on Neural Networks, San Diego, CA, 1990, pp. 279-284.
10. A. Krzyzak, W. Dai, C.Y. Suen, Unconstrained handwritten character classification using modified backpropagation model, in: Proceedings of the First International Workshop on Frontiers in Handwriting Recognition, Montreal, Canada, 1990, pp. 155-166.
11. Lam L., Suen C.Y. Structural classification and relaxation matching of totally unconstrained handwritten ZIP-code numbers. Pattern Recognition. 21(1):1988;19-31.
12. Le Cun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W., Jackel L.D. Handwritten digit recognition with a back-propagation network. Adv. Neural Infor. Processing Syst. 2:1990;396-404.
13. R. Legault, C.Y. Suen, Contour tracing and parametric approximations for digitized patterns, in: A. Krzyzak, T. Kasvand, C.Y. Suen (Eds.), Computer Vision and Shape Recognition, World Scientific, Singapore, 1989, pp. 225-240.
14. B. Lemarie, Practical implementation of a radial basis function network for handwritten digit recognition, in: Proceedings of the Second International Conference on Document Analysis and Recognition, Tsukuba, Japan, 1993, pp. 412-415.
15. Li T., Tang Y.Y., Fang L.Y. A structure-parameter-adaptive (SPA) neural tree for the recognition of large character set. Pattern Recognition. 28(3):1995;315-329.
16. Mai T., Suen C.Y. A generalized knowledge-based system for the recognition of unconstrained handwritten numerals. IEEE Trans. Systems Man Cybernet. 20(4):1990;835-848.
17. Pratt W.K. Digital Image Processing. 1978;Wiley, New York.
18. Sanger T.D. A tree-structured adaptive network for function approximation in high-dimensional spaces. IEEE Trans. Neural Networks. 2(2):1991;285-293.
19. C.Y. Suen, C. Nadal, T. Mai, R. Legault, L. Lam, Recognition of handwritten numerals based on the concept of multiple experts, in: Proceedings of the First International Workshop Frontiers in Handwriting Recognition, Montreal, Canada, 1990, pp. 131-144.