Counter-clustering for training pattern selection

Computer Journal

Volumn 43, Issue 3, 2000, Pages 177-190

  Tambouratzis, Tatiana ^a  

^a INSTITUTE OF NUCLEAR TECHNOLOGY AND RADIATION PROTECTION   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION SUPPORT SYSTEMS; LEARNING SYSTEMS; NEURAL NETWORKS; SET THEORY;

COUNTER CLUSTERING; TRAINING PATTERN SELECTION;

PATTERN RECOGNITION;

EID: 0033684781     PISSN: 00104620     EISSN: None     Source Type: Journal    
DOI: 10.1093/comjnl/43.3.177     Document Type: Article

References (35)

1. Dasarathy, B. V. (1991) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society, Los Alamitos, CA.
2. Cachin, C. (1994) Pedagogical pattern selection strategies. Neural Networks, 7, 175-181.
3. Munro, P. W. (1992) Repeat until bored: a pattern selection strategy. In Moody, J. E., Hanson, S. J. and Lippman, R. P. (eds), Advances in Neural Information Processing 4. Morgan Kauffmann Publishers, San Mateo, CA, pp. 1001-1008.
4. Hart, P. E. (1968) The condensed nearest neighbor rule. IEEE Trans. Information Theory, 4, 515-516.
5. Swonger, C. W. (1972) Sample set condensation for a condensed nearest neighbor decision rule for pattern recognition. In Watanabe, S. (ed.), Frontiers of Pattern Recognition. Academic Press, New York, NY, 511-519.
6. Gates, G. W. (1972) The reduced nearest neighbor rule. IEEE Trans. Information Theory, 18, 431-433.
7. Chang, C. L. (1974) Finding prototypes for NN classifiers. IEEE Trans. Computers, 23, 1179-1184.
8. Wilson, D. (1972) Asymptotic properties of NN rules using edited data. IEEE Trans. Syst., Man, Cyber., 2, 408-421.
9. Ritler, G., Woodruff, H., Lowry, S. and Isenhour, T. (1975) An algorithm for a selective NN decision rule. IEEE Trans. Information Theory, 21, 665-669.
10. Dasarathy, B. V. (1994) Minimal consistent set (MCS) identification for optimal nearest neighbor decision system design. IEEE Trans. Syst., Man, Cyber., 24, 511-516.
11. Salzberg, S. and Decher, A. L. (1995) Best-case results for nearest-neighbor learning. IEEE Trans. Patterns Analysis Machine Intell., 17, 599-608.
12. Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning representations by back-propagating errors. Nature, 323, 533-536.
13. Haykin, S. (1994) Neural Networks: a Comprehensive Foundation. Macmillan Publishing Co., New York, NY.
14. Smolensky, P. (1986) Information processing in dynamical systems: foundations of harmony theory, In Rumelhart, D. E. and McClelland, J.L. (eds), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. MIT Press, Cambridge, MA, pp. 194-281.
15. Chaudhuri, D., Murthy, C. A., Chaudhuri, B. B. (1994) Finding a subset of representative points in a dataset. IEEE Trans. Syst., Man, Cyber., 24, 1416-1425.
16. Tomek, I. (1976) Two modifications of CNN. IEEE Trans. Syst., Man, Cyber., 6, 769-772.
17. Preparata, F. P. and Shamos, M. I. (1985) Computational Geometry: an Introduction. Springer-Verlag, Wien.
18. Frosini, A., Gori, M. and Priami, P. (1996) A neural network-based model for paper currency recognition and verification. IEEE Trans. Neural Networks, 7, 1482-1490.
19. Intrator, N., Reisfeld, D. and Yeshurun, Y. (1996) Face recognition using a hybrid supervised/unsupervised neural network. Pattern Recog. Lett., 17, 67-76.
20. Spence, S. P. C., Hsu, S. and Pearson, J. (1995) Integrating neural networks with image pyramids to learn target context. Neural Networks, 8, 1143-1152.
21. Fisher, R. A. (1936) The use of multiple measurements in taxonomic problems. Ann. Eugenics, 7, 179-188.
22. Zhu, Q., Cai, Y. and Liu, L. (1999) A global learning algorithm for a RBF network. Neural Networks, 12, 527-540.
23. Tambouratzis, T. (1991) An implementation of a harmony theory network for interpreting line-drawings. Network: Comput. Neural Syst., 2, 443-454.
24. Tambouratzis, T. (1998) A consensus-function artificial neural network for map-colouring. IEEE Trans. Syst., Man, Cyber. B, 28, 721-728.
25. Tambouratzis, T. (1997) A simulated annealing artificial neural network implementation of the n-queens problem. Int. J. Intell. Syst., 12, 739-751.
26. Tambouratzis, T. (1997) Edge crossing minimisation via artificial neural networks. Electron. Lett., 33, 1621-1623.
27. Tambouratzis, T. (1997) Graph planarization employing a harmony theory artificial neural network. Neural Comput. Applications, 6, 116-124.
28. Burrascano, P. and Melis, M. (1992) A neural technique for satellite coverage plans optimization. In Aleksander, I. and Taylor, J. (eds), Artificial Neural Networks, 2. North-Holland, Amsterdam, pp. 1499-1502.
29. Tambouratzis, T. (1998) A decomposition-coordination artificial neural network for satellite broadcast scheduling. Electron. Lett., 34, 1503-1504.
30. Tambouratzis, G., Tambouratzis, T. and Tambouratzis, D. (1997) Neural network versus statistical clustering techniques: a pilot study in a phoneme recognition task. In Ellacott, S. W., Mason, J. C. and Anderson, I. J. (eds), Mathematics of Neural Networks: Models, Algorithms and Applications. Kluwer Academic Publishers, Norwell, MA, pp. 355-360.
31. Tambouratzis, T. (1999) A novel artificial neural network for sorting. IEEE Trans. Syst., Man, Cyber. B, 29, 271-275.
32. Aleksander, I. and Stonham, T. J. (1979) Guide to pattern recognition using random-access memories. Comput. Digital Techniques, 2, 29-40.
33. Tattersall, G. D., Foster, S. and Johnston, R. D. (1991) Single-layer look-up perceptrons. IEE Proc. F, 138, 46-54.
34. Allinson, N. M. and Kolcz, A. R. (1993) Enhanced n-tuple approximators. Proc. Weightless Neural Network Workshop '93. York, UK, April 6-7, 1993, pp. 38-45.
35. Davies, E. R. (1988) Training sets and a priori probabilities with the nearest neighbor method of pattern recognition. Pattern Recognition Lett., 8, 11-13.