Abstract
In this work, the correlation between input-output patterns stored in the memory of the neurons of Virtual Generalizing RAM (VG-RAM) weightless neural networks, or knowledge correlation, is used to improve the performance of these neural networks. The knowledge correlation, detected using genetic algorithms, is used for changing the distance function employed by VG-RAM neurons in their recall mechanism. In order to evaluate the performance of the method, experiments with several well-known datasets were made. The results showed that VG-RAM networks employing knowledge correlation perform significantly better than standard VG-RAM networks.
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Carneiro, R.V., Dias, S.S., Fardin, D., Oliveira, H., Garcez, A.S.d., De Souza, A.F. (2006). Improving VG-RAM Neural Networks Performance Using Knowledge Correlation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_48
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DOI: https://doi.org/10.1007/11893028_48
Publisher Name: Springer, Berlin, Heidelberg
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