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   pattern aggregation 在 计算机软件及计算机应用 分类中 的翻译结果: 查询用时:0.61秒
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pattern aggregation
相关语句
  模式聚合
    Method and Application of Decreasing Text Feature Based on Pattern Aggregation
    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
短句来源
    Text Categorization Rule Extraction Based on Pattern Aggregation and Decision Tree
    基于模式聚合和决策树的文本分类规则抽取
短句来源
    A new method of decreasing the dimension of feature vector by using the theory of pattern aggregation(PA) is presented.
    根据模式聚合理论提出了一种文本特征降维的新方法.
短句来源
  模式聚合
    Method and Application of Decreasing Text Feature Based on Pattern Aggregation
    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
短句来源
    Text Categorization Rule Extraction Based on Pattern Aggregation and Decision Tree
    基于模式聚合和决策树的文本分类规则抽取
短句来源
    A new method of decreasing the dimension of feature vector by using the theory of pattern aggregation(PA) is presented.
    根据模式聚合理论提出了一种文本特征降维的新方法.
短句来源
  “pattern aggregation”译为未确定词的双语例句
    The method firstly reducestext dimension with Pattern Aggregation theory that uses class label, then makes thetext dimension further lower by LSI method.
    它首先用PA理论对文本特征进行初步降维,在此基础上利用LSI方法对文本特征进一步降维,抽取隐藏在文本中的主要语义信息。
短句来源
查询“pattern aggregation”译词为用户自定义的双语例句

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A new method of text classifying by using the theory of word aggregation and decision tree is presenteal. The decision tree is applied to text categorization, which has the advantages of high efficiency of data analysis and easily abstracting the categorization rules that are able to understand. However, decision tree has a defect that is only suitable for low dimension of features. The new method establishes the vector space model of term weight in terms of the theory of Pattern Aggregation, which...

A new method of text classifying by using the theory of word aggregation and decision tree is presenteal. The decision tree is applied to text categorization, which has the advantages of high efficiency of data analysis and easily abstracting the categorization rules that are able to understand. However, decision tree has a defect that is only suitable for low dimension of features. The new method establishes the vector space model of term weight in terms of the theory of Pattern Aggregation, which merges such words as a new feature that has the similar mutuality with each class, and so largely reduces the dimension of the vector space. After that, the decision tree is applied to text categorization. Both the advantage of decision tree and better accuracy of categorization can be acquired.

根据词条聚合和决策树原理,提出了一种文本分类的新方法.决策树分类方法具有出色的数据分析效率和容易抽取易于理解的分类规则等优势,但只能应用于维数较低的特征空间.本方法将与各个类别相关程度相似的词条聚合为一个特征,有效地降低了向量空间的维数,然后再使用决策树进行分类,从而既保证了分类精度又获得了决策树易于抽取分类规则的优势.

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...

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.

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K ohonen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.

In this paper,an improved χ~2 statistic is given,which is used to measure contribution for categorization.The new method establishes the text vector space model in terms of the improved χ~2 statistic and the theory of pattern aggregation,which merges some words as a new feature that has the approximate proportion of contribution for categorization,and so largely reduces the dimension of the vector space.And then,the decision tree is applied to text categorization.Both the understandable categorization...

In this paper,an improved χ~2 statistic is given,which is used to measure contribution for categorization.The new method establishes the text vector space model in terms of the improved χ~2 statistic and the theory of pattern aggregation,which merges some words as a new feature that has the approximate proportion of contribution for categorization,and so largely reduces the dimension of the vector space.And then,the decision tree is applied to text categorization.Both the understandable categorization rules and better accuracy of categorization can be acquired.

本文首先提出一种改进的χ2统计量,以此衡量词条对文本分类的贡献。然后根据模式聚合理论,将对各文本类分类贡献比例相近似的词条聚合为一个特征,建立出文本集的特征向量空间模型。此方法有效地降低了文本特征向量空间的维数。最后使用决策树进行分类,从而既保证了分类精度又获得了决策树易于抽取可理解的分类规则的优势。

 
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