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This paper proposes a means of using a multilayered feedforward neural network to identify the author of a text. The network has to be trained where multilayer feedforward neural network as a powerful scheme for learning complex input-output mapping have been used in learning of the textual descriptors in a paragraphs of an author. The resulting training information we get will be used to identify...
The number of hidden neurons has a great influence on the generalization capability of Multilayer Perceptron Neural Network (MLPNN). The ultimate goal of building a MLPNN is to recognize (or generalize) future unseen sample correctly based on the training from training samples. Therefore, the Localized Generalization Error Model (L-GEM) is adopted in this work to select the architecture of a MLPNN...
This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a memetic Pareto evolutionary approach based on the NSGA2 algorithm (MPENSGA2), where we...
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