Abstract
This paper tests a novel improvement in neural network training by implementing Metaplasticity Multilayer Perceptron (MMLP) Neural Networks (NNs), that are based on the biological property of metaplasticity. Artificial Metaplasticity bases its efficiency in giving more relevance to the less frequent patterns and subtracting relevance to the more frequent ones. The statistical distribution of training patterns is used to quantify how frequent a pattern is. We model this interpretation in the NNs training phase. Wisconsin breast cancer database (WBCD) was used to train and test MMLP. Our results were compared to recent research results on the same database, proving to be superior or at least an interesting alternative.
This work has been supported by National Spanish Ministry (MICINN) under the project: PTFNN (MCINN ref: AGL2006-12689/AGR). The author wishes to thank to The National Founda tion of Science Technology and Innovation (FONACIT) of the Bolivariana Republic of Venezuela for its contribution in the development of his PhD doctoral studies.
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Marcano-Cedeño, A., Jevtić, A., Álvarez-Vellisco, A., Andina, D. (2009). New Artificial Metaplasticity MLP Results on Standard Data Base. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_22
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DOI: https://doi.org/10.1007/978-3-642-02478-8_22
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