Journal of the Institute of Electronics Engineers of Korea CI (전자공학회논문지CI)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

LVQ Network Design using SOM

SOM을 이용한 LVQ 네트워크 설계

  • 정경권 (동국대학교 전자공학과) ;
  • 이용구 (한림정보산업대학 전자정보통신과) ;
  • 엄기환 (동국대학교 전자공학과)
  • Published : 2003.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a design method of the LVQ network using the SOM. The proposed method determines subclasses and initial reference vectors of the LVQ network using the SOM. The efficacy of the proposed method is verified by means of simulations on iris data of Fisher and character recognition. The results show that the proposed method improves considerably on the performance of the conventional LVQ network.

본 연구에서는 SOM을 이용하여LVQ 네트워크 설계 방식을 제안한다. 제안한 방식은 SOM을 이용하여 LVQ 네트워크의 서브 클래스를 결정하고 기준 벡터의 초기값을 설정하는 방식으로 LVQ 네트워크의 분류 성능을 향상시킨다. 제안한 방식으로 설계된 LVQ 네트워크의 유용성을 확인하기 위하여 Fisher의 Iris 데이터와 문자 인식에 적용하여 기존 LVQ 네트워크의 초기 기준 벡터를 설정하는 방식들과 비교, 검토한 결과 우수한 분류 성능을 확인하였다.

Keywords

References

  1. T. Kohonen, 'The self-organizing map,' Proceedings of the IEEE, vol. 78, no. 9, pp. 1464-1480, 1990 https://doi.org/10.1109/5.58325
  2. T. Kohonen, 'The self-organizing map,' Neuro-computing, vol. 21, no. 1-3, pp. 1-6, 1998 https://doi.org/10.1016/S0925-2312(98)00030-7
  3. T. Kohonen, 'Generalizations of The Self-organizing map,' Proceedings of International Joint Conference on Neural Networks, vol. 1, pp. 457-462, 1993 https://doi.org/10.1109/IJCNN.1993.713953
  4. T. Kohonen, E. Oja, O. Simula, A. Visa and J. Kangas, 'Engineering applications of the self-organizing map,' Proceedings of the IEEE, vol. 84, no. 10, pp. 1358-1384, 1996 https://doi.org/10.1109/5.537105
  5. A. Baraldi, P. Blonda, F. Parmiggiani, G. Pasquariello and G. Satalino, 'Model transitions in descending FLVQ,' IEEE Transactions on Neural Networks, vol. 9, no. 5, pp. 724-738, 1998 https://doi.org/10.1109/72.712148
  6. J. Vesanto and E. Alhoniemi, 'Clustering of the self-organizing map,' IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 586-600, 2000 https://doi.org/10.1109/72.846731
  7. N.B. Karayiannis, J.C. Bezdek, N.R. Pal, R.J. Hathaway and Pin-I Pai, 'Repairs to GLVQ: a new family of competitive learning schemes,' IEEE Transacitons on Neural Networks, vol. 7, no. 5, pp. 1062-1071, 1996 https://doi.org/10.1109/72.536304
  8. Laurene Fausett, Fundamentals of Neural Networks, Prentice Hall, 1994
  9. Martin T. Hagan, Neural Network Design, Thomson Learning, 1996
  10. Allen Gersho and Robert M. Gray, Vector Quantization and Signal Compression, Kluwer Academic Pub., 1992
  11. S. P. Luttrell, 'Derivation of a class of training algorithms,' IEEE Transactions on Neural Networks, vol. 1, no. 2, pp. 229-232, 1990 https://doi.org/10.1109/72.80234
  12. S. P. Luttrell, 'Code vector density in topographic mappings: Scalar case,' IEEE Transactions on Neural Networks, vol. 2, no. 4, pp. 427-436, 1991 https://doi.org/10.1109/72.88162
  13. H. Ritter, 'Asymptotic level density for a class of vector quantization processes,' IEEE Transactions on Neural Networks, vol. 2, no. 1, pp. 173-175, 1991 https://doi.org/10.1109/72.80310
  14. Tanaka, T. Saito, M., 'Quantitative Properties of Kohonen's Self-Organizing Maps as Adaptive Vector Quantizers(Japanese),' IEICE transacitions on information and systems, D-II, Vol. J75-D-II, No. 6, pp. 1085-1092, 1992
  15. N. Kitajima, 'A new method for initializing reference vectors in LVQ,' IEEE Int. Conf. on Neural Networks, vol. 5, pp. 2775-2779, Nov/Dec. 1995 https://doi.org/10.1109/ICNN.1995.488170