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

The Institute of Electronics and Information Engineers (대한전자공학회)
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Perceptron-like LVQ : Generalization of LVQ

퍼셉트론 형태의 LVQ : LVQ의 일반화

  • Song, Geun-Bae (Department of Electronics Engineering, Ajou University) ;
  • Lee, Haing-Sei (Department of Electronics Engineering, Ajou University)
  • 송근배 (아주대학교 전자공학부) ;
  • 이행세 (아주대학교 전자공학부)
  • Published : 2001.01.26
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Abstract

In this paper we reanalyze Kohonen‘s learning vector quantizing (LVQ) Learning rule which is based on Hcbb’s learning rule with a view to a gradient descent method. Kohonen's LVQ can be classified into two algorithms according to 6learning mode: unsupervised LVQ(ULVQ) and supervised LVQ(SLVQ). These two algorithms can be represented as gradient descent methods, if target values of output neurons are generated properly. As a result, we see that the LVQ learning method is a special case of a gradient descent method and also that LVQ is represented by a generalized percetron-like LVQ(PLVQ).

본 논문에서는 Hebb 학습법에 기초한 Kohonen의 LVQ 학습법을 퍼셉트론 학습에 사용되는 경도 강하 (Gradient descent) 학습법에 의해 재해석한다. Kohonen의 LVQ는 학습법에 따라 두 가지로 나뉠 수 있는데 하나는 자율학습 LVQ(ULVQ)이며 다른 하나는 타율학습 LVQ(SLVQ)이다. 두 경우 모두 출력뉴런의 목표 값을 적당히 생성할 경우 타율학습 경도 강하학습법으로 표현될 수 있다. 결과적으로 LVQ학습법은 타율학습 경도 강하 학습법의 특수한 형태임을 알 수 있으며 또한 LVQ는 보다 일반화된 '퍼셉트론 형태의 LVQ(PLVQ)'알고리즘으로 표현될 수 있음을 알 수 있다. 본 논문에서는 이를 증명하고 결론을 맺는다.

Keywords

References

  1. T. Kohonen, Self -Organizing Maps, Springer-verlag, 1995
  2. T. Kohonen, 'Learning vector quantization [or pattern recognition,' Rep. TKK-F-A601, Helsinki Univ, Technol., Dept. Techn.Phys., Lab. Computer an Inform. Sci., 1986
  3. T. Kohonen, 'versions of learning vector quantization,' in Proc. IEEE int. Joint Conf. on Neur. Networks, vol. 1, pp, 223-228, San Diego, CA, June 1990 https://doi.org/10.1109/IJCNN.1990.137573
  4. D. O. Hebb, Organization of Behavior, New York: Science Editions, 1949
  5. R. Rosenblatt, Principles of Neuro-dynamics, New York: Spartan Books, 1959
  6. D. Rumelhart, J. McClelland and the PDP Research Group, Parallel Distributed Processing: Explorations in the Micro structure of Cognition, MIT Press, Cambridge, MA, Vol. 1, 1986
  7. R. P. Lippman, 'An introduction to computing with neural nets,' IEEE ASSP Magazine, vol, ASSP-38, vol, 4, pp. 4-22, April 1987
  8. X. M. Song, 'A radial basis [unction network for empirical modeling of soil extraction process,' in Proc. of EANN'95 Conf., 1995
  9. B. Widrow, 'sampled data systems, a statistical theory of adaptation,' 1959 IRE WESCON Convention Record, part 4, New York: Institute of Radio Engineers, 1959