D. Dersch and P. Tavan. Asymptotic level density in topological feature maps. IEEE Transactions on Neural Networks, 6(1):230--236, 1995. Home/Search Document Not in Database Summary Related Articles Check |
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Mathematical Aspects of Neural Networks - Hammer, Villmann (2003) (Correct)
.... As mentioned above, the SOM is not an optimal vector quantizer in the sense of the error E [W] 149] Devia tions are due to the incorporation of neighborhood learning and topology preservation [31,141] This fact leads to a different magnification of the SOM in comparison to the usual VQ [39, 107]. Therefore, several modifications of SOM exist to achieve optimal magnification [40] or, more generally, to control the magnification by local learning rates according to local estimates of the data probability density [8] In the winner relaxing magnification control the local learning based on ....
D. Dersch and P. Tavan. Asymptotic level density in topological feature maps. IEEE Trans. on Neural Networks, 6(1):23(236, January 1995.
Kohonen Maps Versus Vector Quantization for Data Analysis - de Bodt, Verleysen, Cottrell (1997) (Correct)
....that the speed of convergence is better than with data obtained by independent random drawings. Another interesting result can be found in [7] and has been completed by many results taking into account the number of neighbours used during learning, the shape of the neighbourhood function, [15, 16]. We know that without weights, the initial distribution cannot be reconstructed exactly. More precisely, 7] shows that the best vector quantization (i.e. which leads to a minimisation of distortion n, without neighbour) corresponds to a discrete density g(x) which converges asymptotically (when ....
Dersch D. and Tavan P., Asymptotic Level Density in Topological Feature Maps, IEEE Transactions on Neural Networks, vol. NN-6, no. 1, pp. 230-236, January 1995. 216
Kohonen Maps Versus Vector Quantization for Data Analysis - de Bodt, Verleysen, Cottrell (1997) (Correct)
....that the speed of convergence is better than with data obtained by independent random drawings. Another interesting result can be found in [7] and has been completed by many results taking into account the number of neighbours used during learning, the shape of the neighbourhood function, [15, 16]. We know that without weights, the initial distribution cannot be reconstructed exactly. More precisely, 7] shows that the best vector quantization (i.e. which leads to a minimisation of distortion x n , without neighbour) corresponds to a discrete density g(x) which converges asymptotically ....
Dersch D. and Tavan P., Asymptotic Level Density in Topological Feature Maps, IEEE Transactions on Neural Networks, vol. NN-6, no. 1, pp. 230-236, January 1995.
Mathematical Aspects of Neural Networks - Hammer (2003) (Correct)
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D. Dersch and P. Tavan. Asymptotic level density in topological feature maps. IEEE Transactions on Neural Networks, 6(1):230--236, 1995.
Controling the Magnification Factor of Self-Organizing.. - Bauer, Der, Herrmann (1995) (Correct)
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D.R. Dersch, P. Tavan, 1995. Asymptotic level density in topological feature maps, IEEE TNN 6, 230-236.
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