New preprocessing methods for holographic neural networks (CROSBI ID 516089)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Manger, Robert ; Souček, Branko
engleski
New preprocessing methods for holographic neural networks
We propose two new methods for preprocessing of stimulus data in holographic neural networks. The first method achieves optimal data symmetrization, and it is based on estimating the actual distributions of elements within a stimulus vector. The second method serves for data expansion, and it relies on sine and cosine functions to produce dummy stimulus elements. We describe an implementation of our methods and report on some experiments. Our proposals improve the applicability of holographic networks. Namely, symmetrization assures accuracy in reproducing learned stimulus-response associations, while expansion increases learning capacity.
holographic neural networks; data preprocessing; stimulus expansion; stimulus symmetrization
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Podaci o prilogu
190-197-x.
1993.
objavljeno
Podaci o matičnoj publikaciji
Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms (ANNGA '93)
Albrecht, R.F ; Reeves, C.R ; Steele, N.C.
Beč: Springer
Podaci o skupu
International Confeerence on Artificial Neural Nets and Genetic Algorithms (ANNGA '93)
predavanje
14.04.1993-16.04.1993
Innsbruck, Austrija