Document Classification Based on Improved QPSO and RBF Neural Networks
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    Abstract:

    To enhance the accuracy of the text classification, a new method based on quantum PSO and RBF neural network is proposed. Firstly, it establishes the key words set to describe the classification of the samples, and uses fuzzy vector space model to build the feature vectors of every kind of sample, then automatically classifies the texts by RBF neural network, optimizes the parameters of RBF neural network by improved quantum PSO to enhance its approximation capability. The new method is proved by the classification of some documents in China periodical document database. The experiment shows that this method makes significant improvements in classification accuracy compared to other methods.

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李滨旭,姚姜虹.基于改进QPSO和RBF神经网络的文本分类方法.计算机系统应用,2016,25(7):264-267

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  • Received:January 07,2016
  • Revised:February 26,2016
  • Adopted:
  • Online: July 21,2016
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