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Gamma-filter self-organising neural networks for unsupervised sequence processing

Gamma-filter self-organising neural networks for unsupervised sequence processing

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Adding γ-filters to self-organising neural networks for unsupervised sequence processing is proposed. The proposed γ-context model is applied to self-organising maps and neural gas networks. The γ-context model is a generalisation that includes as a particular example the previously published merge-context model. The results show that the γ-context model outperforms the merge-context model in terms of temporal quantisation error and state-space representation.

References

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      • M.S. Salhi , N. Arous , N. Ellouze . Principal temporal extensions of SOM: overview. Int. J. Signal Process. Image Process. Pattern Recognit. , 3 , 121 - 144
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      • Strickert, M., Hammer, B.: `Neural gas for sequences', Proc. Workshop on Self-Organizing Networks, (WSOM), 2003, Kyushu, Japan, p. 53–58.
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