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Published in:
2009 | OriginalPaper | Chapter
New Artificial Metaplasticity MLP Results on Standard Data Base
Authors : Alexis Marcano-Cedeño, Aleksandar Jevtić, Antonio Álvarez-Vellisco, Diego Andina
Published in: Bio-Inspired Systems: Computational and Ambient Intelligence
Publisher: Springer Berlin Heidelberg
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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.