New Directions in Statistical Signal Processing: From Systems to Brains (Neural Information Processing)
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
A linear learning method for multilayer perceptrons using least-squares
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Training of multilayer perceptron neural networks by using cellular genetic algorithms
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
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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.