Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Dynamic Extension of Genetic Tree Maps

유전 목 지도의 동적 확장

  • 하성욱 ((주)이지하모니 기술연구소) ;
  • 권기향 (동아대학교 컴퓨터공학과) ;
  • 강대성 (동아대학교 전기전자컴퓨터공학부)
  • Published : 2002.06.01
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Abstract

In this paper, we suggest dynamic genetic tree-maps(DGTM) using optimal features on recognizing data. The DGTM uses the genetic algorithm about the importance of features rarely considerable on conventional neural networks and introduces GTM(genetic tree-maps) using tree structure according of the priority of features. Hence, we propose the extended formula, DGTM(dynamic GTM) has dynamic functions to separate and merge the neuron of neural network along the similarity of features.

본 논문에서는, 인식될 데이타에서 최적 특징을 구성할 수 있는 새로운 신경망 구조인 동적 유전 트리맵(DGTM)을 제안한다. DGTM은 기존의 신경망(neural networks)에서 고려되지 못한 데이터의 특징(feature)에 대한 중요도를 유전 알고리즘(genetic algorithm)으로 구성하고, 특징의 우선순위에 따라 트리 구조를 도입한 GTM(genetic tree-map)을 적용한다. 데이타의 유사성에 따라서 신경망의 뉴런이 동적으로 분리되고 병합될 수 있도록 동적인 기능을 갖는 DGTM(dynamic GTM)으로 확장한 방식을 제안한다.

Keywords

References

  1. D. E. Goldberg, Genetic Algorithm in Search Optimization and Machine Learning, Addision Wesley, 1989
  2. L. D. Davis, The Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991
  3. N. V. Subba Reddy and P. Nagabhushan, 'A Three-dimensional Neural Network Model for Unconstrained Handwritten Numeral Recognition,' Pattern Recognition, Vol. 31, No. 5, pp. 511-516, 1998 https://doi.org/10.1016/S0031-3203(97)00075-7
  4. Zheru Chi, Jing Wu and Hong Yan, 'Handwritten Numeral Recognition Using Self-Organizing Maps and Fuzzy Rules,' Pattern Recognition, Vol. 28, No. 1, pp. 59-66, 1995 https://doi.org/10.1016/0031-3203(94)00085-Z
  5. Sung-Bae Cho, 'Recognition of Unconstrained Handwritten Numerals by Self-Organizing Neural Network,' Proceedings of ICPR'96, pp. 426-430, 1996
  6. Y. S. Huang and C. Y. Suen, 'A Methode Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals,' IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 17, No. 1, pp. 90-94, January 1995 https://doi.org/10.1109/34.368145
  7. L. Xu, A. Krzyzak, and C. Y. Suen, 'Method of Combining Multiple Classifiers and Their Application to Handwritten Numeral Recognition,' IEEE Transaction on Systems, Vol. SMC-22, No. 3, pp. 418-435 1992 https://doi.org/10.1109/21.155943
  8. T. K. Suen, R. Legault, C. Nadal, M. Cheriet, and L. Lam, 'Building a New Generation of Handwritting Recognition Systems,' Pattern Recognition Letters, Vol. 14, No.4, pp. 305-315, 1993 https://doi.org/10.1016/0167-8655(93)90096-V
  9. J. Pearl, Probabilistic Reasoning in Intelligent System: Network of Plausible Inference, Morgan Kaufmann Publishers Inc., San Mateo, California,1998
  10. M. D. McLeish, P. Yao, and T. Stirtzinger, 'A Study on the Use of Belief Functions for Medical Expert Systems,' Journal of Applied Statistics, Vol. 18, No.1, pp155-174, 1991 https://doi.org/10.1080/02664769100000013
  11. Yoshihiko Hamamoto, Shunji Uchimura, Masanori Watanabe, Tetsuya Yasuda and Shigo Tomita, 'Recognition of Handwritten Numerals Using Gabor Features,' Proceedings of ICPR'96, pp. 250-253, 1996
  12. Dahai Cheng and Hong Yan, 'Recognition of Handwritten Numeral Base on Contour Information,' Pattern Recognition, Vol. 3, No. 1, pp. 235-255, 1998 https://doi.org/10.1016/S0031-3203(97)00046-0
  13. Sven Behnke and Marcus Pfister, 'Recognition of Handwritten Digits Using Structural Information,' Proceedings of ICNN'97, Vol. 4, pp. 1391-1396, 1997 https://doi.org/10.1109/ICNN.1997.613997
  14. R. Legault and C. Y. Suen, 'Contour Tracking and Parametric Approximations for the Digitized Patterns,' Computer Vision and Shape Recognition : Singapore, pp. 225-240, 1989
  15. A. Krzyzak, W. Dai and C. Y. Suen, 'Unconstrained Handwritten Character Classification using Modified Backpropagation Model,' In Proc. 1st Int. Workshop on Frontiers in Handwriting Recognition, pp. 155-166, 1990
  16. C. Y. Seun, C. Nadal, R. Legault, T. A. Mai and L. Lam, 'Computer Recognition of Unconstrained Handwritten Numerals,' Proceeding of the IEEE. Vol. 80, No. 7, pp. 1162-1180, 1992 https://doi.org/10.1109/5.156477
  17. T. Mai and C. Y. Seun, 'A General Knowledge-based System for the Recognition of Unconstrained Handwritten Numerals,' IEEE Trans. on Systems, Vol. 20, No. 4, pp. 835-848, 1990 https://doi.org/10.1109/21.105083