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Aiming at improving the generalization performance of the dynamic convolutional neural network on text sentiment classification, a dynamic convolutional extreme learning machine algorithm was proposed. This algorithm modified the output layer of dynamic convolutional neural network by replacing the fully connection layer with the shallow random neural network. By utilizing the perturbation ability of the random generation of parameters, it is prone to mitigate the dependence on training samples and avoid over-fitting to improve the classification performance. Experiments on several public data sets show that this approach outperforms the dynamic convolutional neural network and extreme learning machine under the evaluation metrics including accuracy rate, F1-measure, etc. © 2017, Editorial Department of Journal of Beijing University of Technology. All right reserved.
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