On-line pattern analysis by evolving self-organizing maps

Neurocomputing

Volumn 51, Issue , 2003, Pages 87-103

  Deng, Da ^a   Kasabov, Nikola ^a  

^a UNIVERSITY OF OTAGO   (New Zealand)

Author keywords

Classification; Clustering; On line learning; Self organizing

Indexed keywords

ALGORITHMS; DATA PROCESSING; LEARNING SYSTEMS; PATTERN MATCHING; VECTOR QUANTIZATION;

ON-LINE PATTERN ANALYSIS;

SELF ORGANIZING MAPS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; CLUSTER ANALYSIS; EQUIPMENT DESIGN; IMAGE ANALYSIS; IMAGE PROCESSING; INFORMATION SYSTEM; INTELLIGENCE; INTERMETHOD COMPARISON; LEARNING; MATHEMATICAL MODEL; ONLINE SYSTEM; PATTERN RECOGNITION; PRIORITY JOURNAL; QUALITY CONTROL;

EID: 0037382210     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0925-2312(02)00599-4     Document Type: Article

References (27)

1. M. Benaim, L. Tomasini, Approximating functions and predicting time-series with multi-sigmoidal basis functions, in: I. Aleksander, J. Taylor (Eds.), Artificial Neural Networks, Vol. 2, Elsevier, Amsterdam, 1992, pp. 407-411.
2. J. Bruske, G. Sommer, Dynamic cell structure learns perfectly topology preserving map, Neural Comput. 7 (1995) 845-865.
3. D. Chaudhuri, C.A. Murthy, B.B. Chaudhuri, A modified metric to compute distance, Pattern Recognition 7 (1992) 667-677.
4. D. Deng, N. Kasabov, ESOM: an algorithm to evolve self-organizing maps from on-line data streams, Proceedings of IJCNN'2000, Vol. VI, IEEE Press, New York, 2000, pp. 3-8.
5. R. Duda, P. Hart, D. Stork, Pattern Classification, 2nd Edition, Wiley, New York, NY, 2000.
6. G.W. Flake, Squared unit augmented, radially extended, multilayer perceptrons, in: G.B. Orr, K.-R. Muller (Eds.), Neural Networks: Tricks and Trade, Lecture Notes in Computer Science, Vol. 1524, Springer, Berlin, 1998, pp. 145-163.
7. B. Fritzke, Growing cell structures - a self-organizing network for unsupervised and supervised learning, Neural Networks 7 (1994) 1441-1460.
8. B. Fritzke, A growing neural gas network learns topologies, in: D. Touretzky, T.K. Keen (Eds.), Advances in Neural Information Processing Systems, Vol. 7, MIT Press, Cambridge, MA, 1995, pp. 625-632.
9. M. Gervautz, W. Purgathofer, A simple method for color quantization: octree quantization, in: A. Glassner (Ed.), Graphics Gems, Academic, New York, 1990, pp. 287-293.
10. P. Heckbert, Color image quantization for frame buffer display, Comput. Graphics (SIGGRAPH) 16 (1982) 297-307.
11. D. Heinke, F.H. Hamker, Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP, IEEE Trans. Neural Networks 9 (1998) 1279-1291.
12. T.M. Heskes, B. Kappen, Learning processes in neural networks, Phys. Rev. A 44 (1991) 2718-2726.
13. T. Kohonen, Self-organizing Maps, 2nd Edition, Springer, Berlin, 1997.
14. S. Lawrence, A.C. Tsoi, A.D. Back, Function approximation with neural networks and local methods: bias, variance and smoothness, in: P. Barlett, et al., (Eds.), Proceedings of Australian Conference on Neural Networks, ANU Press, Canberra, 1996, pp. 16-21.
15. J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the Fifth Berkeley Symposium on Mathematics, UC Press, Berkeley, 1967, pp. 281-297.
16. J. Mao, A.K. Jain, Artificial neural networks for feature extraction and multivariate data projection, IEEE Trans. Neural Networks 6 (1995) 296-317.
17. J.L. Marroquin, F. Girosi, Some extension of the K-means algorithm for image segmentation and pattern classification, Technical Report 1390, Massachusetts Institute of Technology, 1993.
18. T.M. Martinetz, S.G. Berkovich, K.J. Schulten, Neural-gas network for vector quantization and its application to time-series prediction, IEEE Trans. Neural Networks 4 (1993) 558-569.
19. T. Martinetz, K. Schulten, Topology representing networks, Neural Networks 7 (1994) 507-522.
20. S.J. Nowlan, Maximum likelihood competitive learning, in: D. Touretzky (Ed.), Advances in Neural Information Processing Systems, Vol. 2, Morgan Kauffman, New York, 1990, pp. 574-582.
21. A.J. Robinson, Dynamic error propagation networks, Ph.D. Thesis, Cambridge University, Cambridge, 1989.
22. K. Rose, E. Gurewitz, G.C. Fox, Statistical mechanics and phase transitions in clustering, Phys. Rev. Lett. 65 (1990) 945-948.
23. J. Sammon Jr., A non-linear mapping for data structure analysis, IEEE Trans. Comput. 18 (1969) 401-409.
24. N. Shang, L. Breiman, Distribution based trees are more accurate, in: Proceedings of ICONIP'96, Springer, Hong Kong, 1996, pp. 133-138.
25. B. Ster, A. Dobnikar, Neural networks in medical diagnosis: comparison with other methods, in: A. Bulsari, et al. (Eds.), Proceedings of the International Conference on Engineering Applications with Neural Networks, London, 1996, pp. 427-430.
26. X. Wu, Color quantization by dynamic programming and principal analysis, ACM Trans. Graphics 11 (1992) 348-372.
27. C.T. Zahn, Graph-theoretical methods for detecting and describing Gestalt clusters, IEEE Trans. Comput. C-20 (1971) 68-86.