基于模式聚合理论的文本特征降维方法及其在文本分类中的应用

根据模式聚合理论提出了一种文本特征降维的新方法.结合动态Kohonen网络理论检验了文本分类效果.在网络训练阶段引入了监督机制,提高了网络的分类速度和精度.应用模式聚合(PA)理论建立文本集的向量空间模型,从分类贡献的角度强化了词条的作用,消减了原词条矩阵中包含的冗余模式,有效地降低了向量空间的维数,提高了文本分类的精度和速度,并通过实验证明了该方法的泛化能力.

Method and Application of Decreasing Text Feature Based on Pattern Aggregation

A new method of decreasing the dimension of feature vector by using the theory of pattern aggregation(PA) is presented.The method connected Kohonen network acquire better result of text categorization.The Kohonen network is applied to realize text classifying,and apply supervising method to network training.Therefore,the speed and the precision of classifying are improved.However,to the text vector of high dimension,the speed of classifying is still very slow using Kohonen network.Even the result of classifying cannot be acquired.The new method establishes vector space model of term weight by the theory of PA,which enhances the function of the words from the viewpoint of categorization effect,and decreases the dimension of vector by eliminating redundant features.Therefore the new method advances the speed and the precision of text categorization largely,and the method has better generalization ability,which is approved by the experimentation.