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The Metaplasticity is an inherent property of the Biological neuron connections that consists in the capacity of modifying the learning mechanism using the information present in the network itself during the training. This concept can be applied to Artificial Learning Algorithms using a technique called Artificial Metaplasticity. The idea is to improve the results in Machine Learning taking as the...
In this work we tested and compared artificial metaplasticity (AMP) results for multilayer perceptrons (MLPs). AMP is a novel artificial neural network (ANN) training algorithm inspired on the biological metaplasticity property of neurons and Shannon's information theory. During training phase, AMP training algorithm gives more relevance to less frequent patterns and subtracts relevance to the frequent...
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...
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