Artificial metaplasticity MLP applied to image classification

In this paper we apply Artificial Metaplasticity to a Multilayer Perceptron (MLP) for image classification. Artificial Metaplasticity is a novel Artificial Neural Network (ANN) training algorithm that gives more relevance to less frequent training patterns and subtracts relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the MLP performance. In this paper, we test Metaplasticity MLP (MMLP) algorithm on an image standard data set: theWisconsin Breast Cancer Database (WBCD). WBCD is a well-used database in Machine Learning, ANN and Signal Processing. Experimental results show that MMLPs reach better accuracy than any other recent results.

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