Quantification Analysis Problem using Mean Field Theory in Neural Network


The Transactions of the Korea Information Processing Society (1994 ~ 2000), Vol. 2, No. 3, pp. 417-424, Apr. 1995
10.3745/KIPSTE.1995.2.3.417,   PDF Download:

Abstract

This paper describes MFT(Mean Field Theory) neural network with continuous variables is applied to quantification analysis problem. A quantification analysis problem, one of the important problems in statistics, is NP complete and arises in the optimal location of objects in the design space according to the given similarities only. This paper presents a MFT neural network with continuous variables for the quantification problem. Starting with reformulation of the quantification problem to the penalty problem, this paper propose a "one-variable stochastic simulated annealing(one-variable SSA)" based on the mean field approximation. This makes it possible to evaluate of the spin average faster than real value calculating in the MFT neural network with continuous variables. Consequently, some experimental results show the feasibility of this approach to overcome the difficulties to evaluate the spin average value expressed by the integral in such models.


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Cite this article
[IEEE Style]
C. K. Soo, "Quantification Analysis Problem using Mean Field Theory in Neural Network," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 2, no. 3, pp. 417-424, 1995. DOI: 10.3745/KIPSTE.1995.2.3.417.

[ACM Style]
Cho Kwang Soo. 1995. Quantification Analysis Problem using Mean Field Theory in Neural Network. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 2, 3, (1995), 417-424. DOI: 10.3745/KIPSTE.1995.2.3.417.