N.B. Karayiannis and J.C. Bezdek, An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering, IEEE Transactions on Fuzzy Systems 5(4) (1997), 622--628.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....units within the external lattice. Recent advances in the field have presented a broad family of batch FLVQ algorithms formally defined as a class of cost function minimization schemes. Hereafter, this class of batch vector quantizers will be referred to as the Extended FLVQ Family (EFLVQ F) 55] [56], 57] FLVQ updating can be seen as a special case of EFLVQ F learning schemes for a restricted range of the weighting exponent. FLVQ is also related to several on line fuzzy clustering algorithms such as the sequential Generalized LVQ (GLVQ) 58] and GLVQ Family algorithms (GLVQ F) 59] and the ....

....asymptotic case B, i.e. FLVQ behaves consistently with the second Kohonen constraint. These theoretical conclusions about transitions of the FLVQ learning strategy as a function of decreasing m(e) increasing epoch time e) are consistent with those regarding FCM, EFLVQ F and GLVQ F [55] [56], 57] 59] all systems employing Equation (14) as their relative membership function (see Table 1) This analysis is also consistent with the heuristic choice 7 m 0 m f 1:1 recommended in [6] On the other hand, learning rate values do not necessarily decrease with time, i.e. FLVQ does ....

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N. B. Karayiannis, J. C. Bezdek, "An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering", IEEE Trans. on Fuzzy Systems, vol. 5, no. 4, pp. 622-628, 1997.

....units within the external lattice. Recent advances in the field have presented a broad family of batch FLVQ algorithms formally defined as a class of cost function minimization schemes. Hereafter, this class of batch vector quantizers will be referred to as the Extended FLVQ Family (EFLVQ F) [55], 56] 57] FLVQ updating can be seen as a special case of EFLVQ F learning schemes for a restricted range of the weighting exponent. FLVQ is also related to several on line fuzzy clustering algorithms such as the sequential Generalized LVQ (GLVQ) 58] and GLVQ Family algorithms (GLVQ F) 59] ....

....learning Width of the learning rate GLVQ 3 , GLVQ F 2 distrib. is constant FCM, EFLVQ F 1 FALVQ Width of the learning rate distrib. decreases with time FLVQ Cooling schedule VQ, FALVQ, learning rate decreases with time) EFLVQ F 4 GLVQ, GLVQ F 5 No cooling schedule FCM, FLVQ 1 see [55], p. 252 (m = 2) 2 see [59] p. 1068 (m = 2) 3 extended to the entire net. 4 see [55] p. 251. 5 see [57] p. 33. 10.1 Input parameters ffl FLVQ requires the user to define number c of natural groups to be detected. ffl It requires the initial and final weighting exponent m 0 and m f , ....

[Article contains additional citation context not shown here]

N. B. Karayiannis, and M. Ravuri, "An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering", in Intelligent Engineering Systems Through Artificial Neural Networks, C. H. Dagli et al., Eds., vol. 5, New Yoork, NY: ASME Press, pp.247-252,1995.

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N.B. Karayiannis and J.C. Bezdek, An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering, IEEE Transactions on Fuzzy Systems 5(4) (1997), 622--628.

No context found.

N. B. Karayiannis and J. C. Bezdek, "An integrated approach to fuzzy learning vector quantization and fuzzy c-means clustering," IEEE Transactions on Fuzzy Systems, vol. 5, pp. 622--628, 1997.

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