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Research on Text-Reducing Method Based on the Improved KNN Algorithm

机译:基于改进的KNN算法的文本约简方法研究

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There are relevance and redundancy of the feature words in the text vector space, so we proposed a text-reducing method based on the improved KNN algorithm in this paper. Vector polymer theory and feature selection methods were used to reducing the dimension of vector space. Feature words would have more ability to represent categories after feature selection. Experiments proved, the improved KNN algorithm were used in text-reducing not only can reducing the dimension of vector space more effectively, but also can improving the speed and accuracy of the text classify.
机译:文本向量空间中特征词具有相关性和冗余性,因此本文提出了一种基于改进的KNN算法的文本约简方法。使用矢量高分子理论和特征选择方法来减小矢量空间的维数。选择特征后,特征词将具有更多的表示类别的能力。实验证明,改进的KNN算法用于文本约简,不仅可以有效地减小向量空间的维数,而且可以提高文本分类的速度和准确性。

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