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pattern aggregation
相关语句
  模式聚合
    Onthe basis of data analysis the improved pattern aggregation method is presented. Andthe neural network is used to calculate the weight of each dimension of VSM modelsto overcome such a shortcoming of VSM space as possessing the same weight byeach dimension. Therefore it can increase the text classification precision of theimproved KNN algorithm on the basis of decreasing of the complexity of time andspace.
    此外本论文提出了一种基于模式聚合和各维不同权重的改进KNN文本分类算法,在数据分析的基础上提出优化的模式聚合方法,并利用神经网络计算空间各维不同权重以克服VSM空间各维权重相等的缺点,可以在降低时间和空间复杂度的基础上,提高KNN算法的文本分类准确度。
短句来源
    In view of the inadequacy of K nearest neighborhood (KNN) algorithm in text-processing environment in vector space models,this paper puts forward an improved KNN method of text categorization in accordance with self-organization mapping neutral network theory(SOM),feature selection theory and pattern aggregation theory.
    本文针对VSM (向量空间模型)中KNN (K最近邻算法)在文本处理环境下的不足,根据SOM (自组织映射神经网络)理论、特征选取和模式聚合理论,提出了一种改进的KNN文本分类方法。
短句来源
    This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.
    应用特征选取和模式聚合理论以降低特征空间维数。
短句来源
  模式聚合
    Onthe basis of data analysis the improved pattern aggregation method is presented. Andthe neural network is used to calculate the weight of each dimension of VSM modelsto overcome such a shortcoming of VSM space as possessing the same weight byeach dimension. Therefore it can increase the text classification precision of theimproved KNN algorithm on the basis of decreasing of the complexity of time andspace.
    此外本论文提出了一种基于模式聚合和各维不同权重的改进KNN文本分类算法,在数据分析的基础上提出优化的模式聚合方法,并利用神经网络计算空间各维不同权重以克服VSM空间各维权重相等的缺点,可以在降低时间和空间复杂度的基础上,提高KNN算法的文本分类准确度。
短句来源
    In view of the inadequacy of K nearest neighborhood (KNN) algorithm in text-processing environment in vector space models,this paper puts forward an improved KNN method of text categorization in accordance with self-organization mapping neutral network theory(SOM),feature selection theory and pattern aggregation theory.
    本文针对VSM (向量空间模型)中KNN (K最近邻算法)在文本处理环境下的不足,根据SOM (自组织映射神经网络)理论、特征选取和模式聚合理论,提出了一种改进的KNN文本分类方法。
短句来源
    This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.
    应用特征选取和模式聚合理论以降低特征空间维数。
短句来源
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In view of the inadequacy of K nearest neighborhood (KNN) algorithm in text-processing environment in vector space models,this paper puts forward an improved KNN method of text categorization in accordance with self-organization mapping neutral network theory(SOM),feature selection theory and pattern aggregation theory.This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.And because each dimension of VSM models possesses the same weight,which...

In view of the inadequacy of K nearest neighborhood (KNN) algorithm in text-processing environment in vector space models,this paper puts forward an improved KNN method of text categorization in accordance with self-organization mapping neutral network theory(SOM),feature selection theory and pattern aggregation theory.This paper employs feature selection theory and pattern aggregation theory to reduce feature space dimension.And because each dimension of VSM models possesses the same weight,which is not suitable for text-processing environment,this paper suggests applying SOM neutral network to calculate the weight of each dimension of VSM models.Combining the two improvements,this paper efficiently reduces the dimensions of vector space and raises accuracy and speed of text categorization.

本文针对VSM (向量空间模型)中KNN (K最近邻算法)在文本处理环境下的不足,根据SOM (自组织映射神经网络)理论、特征选取和模式聚合理论,提出了一种改进的KNN文本分类方法。应用特征选取和模式聚合理论以降低特征空间维数。传统的VSM模型各维相同的权重并不适应于文本处理的环境,本文提出应用SOM神经网络进行VSM模型各维权重的计算。结合两种改进,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。

 
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