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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
     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
     In this paper, three methods for text feature dimensions reduction are presented: The first method reduces the dimensions based on pattern aggregation theory and an improvedχ~2 statistic, and then the better accuracy of categorization is acquired;
     本文提出了三种特征降维方法:一种是基于模式聚合和改进χ~2统计量的文本降维方法,有效地降低文本维数并可提高分类精度;
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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  “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
     The results indicated that the spatial distribution pattern and aggregation intensity were different in different environmental conditions,while the tendency of pattern aggregation was generally parallel. The figure of pattern scale and pattern intensity showed that plot 2 clumped in 25 and 100 m2,and plot 3 clumped in 150 m2,while plot 1 performed the pattern of random distribution in all quadrat scale.
     样地2在25和100m2的范围内集群分布,样地3在150m2范围内集群分布,样地1在7个取样面积下,均成随机分布。
短句来源
  相似匹配句对
     pattern
     格调之美
短句来源
     are good methods for detecting the aggregation pattern.
     Iwao回归与Taylor幂法则也是确定聚集型分布的好方法,结果都说明是聚集型的。
短句来源
     Aggregation was the main spatial distribution pattern.
     群聚分布是各组成种的主要分布方式。
短句来源
     Pattern Electroplating
     图文电镀
短句来源
     The aggregation of C.
     通过观察不同条件下C.
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In most of the reported studies on spatial pattern of insect populations,the natural habitat unit (NHU) of population individuals is essentially assumed as point,i.e., the number of individuals within a NHU as basic unit for surveying,assessing and interpreting spatial pattern, But in fact the NHU of insect population is generally a multidimensional structure rather than point. Therefore the much spatial information may be lost owing to this inappropriate assumption. In this paper, pine tree,the NHU of Dendrolimus...

In most of the reported studies on spatial pattern of insect populations,the natural habitat unit (NHU) of population individuals is essentially assumed as point,i.e., the number of individuals within a NHU as basic unit for surveying,assessing and interpreting spatial pattern, But in fact the NHU of insect population is generally a multidimensional structure rather than point. Therefore the much spatial information may be lost owing to this inappropriate assumption. In this paper, pine tree,the NHU of Dendrolimus tabulaeformis,is considered as one-dimensional axis composed of strata of verticillate-branches(SVBs) rather than points. Based on the above idea,spatial patterns of Dendrollmus tabulaeformis larvae and pupae on every SVB were assessed and interpreted with Taylor's power law model and the author's reinterpreted Taylor's power law,and the law of spatial patterns varying among strata was analysed with Fuzzy Clustering Analysis (FCA),Grey Clustering Analysis (GCA) and Trend Curved Surface Analysis (TCSA)methods. Also within-pine-tree vertical distribution aggregation of population was discussed. It coneiudes that: (1)according to the author's reinterpreted Taylor's power law, the spatial pattern of larvae and pupae on every SVB follows inverse densitydependent aggregation,and the critical density m of the population aggregation was calculated, so the spatial pattern continuum on every SVB can be deseribed quantitatively; (2)the resuits of FCA,GCA,and TCSA show that the alw of spatial pattern aggregation varying among Strata is non-liner and oscillatory,and the Strata can be clustered into several types according to the characteristics of the population aggregation; (3)within-pine-tree vertical distribution also follows inverse density-dependent aggregation.

将油松植株看作是处于不同空间层次的亚生境单元——“轮枝层”构成的一个多层次立体结构,以Taylor幂法则模型及笔者重新解释的Taylor幂法则,作为描述种群空间格局的基本模型和准则。采用模糊聚类分析,灰色聚类分析,趋势面分析等方法,研究油松毛虫在各轮枝层分布的空间格局及其在各层间的变化规律;并测定了种群个体在油松植株内层间垂直分布的种群聚集度。结果表明:(1)种群在各轮枝层分布格局的种群聚集度,均为逆密度制约型,并给出了种群聚集临界密度m_0的值,因而可以对各轮枝层的空间格局连续统作出定量描述;(2)各轮枝层的种群聚集度在各层间的变化是非线性的;可以按聚集特征参数划分一定类别;(3)种群个体在植株内垂直分布的种群聚集度也是逆密度制约型。

The module morphological characteristics of Leymus chinensis, Phragmites communis and Kalimeris integrifolia were measured and analyzed by sampling randomly in thirty sites. To explore the mechanism of competition among main plant species on grasslands in Changling County. Fifty L.chinensis plants in each L.chinensis, L.chinensis + Ph.communis and L. chinensis + K.integrifolia community, twenty K.integrifolia plants in each K.integrifolia, L.chinensis+ K.integrifolia and Ph.communis + K.integrifolia...

The module morphological characteristics of Leymus chinensis, Phragmites communis and Kalimeris integrifolia were measured and analyzed by sampling randomly in thirty sites. To explore the mechanism of competition among main plant species on grasslands in Changling County. Fifty L.chinensis plants in each L.chinensis, L.chinensis + Ph.communis and L. chinensis + K.integrifolia community, twenty K.integrifolia plants in each K.integrifolia, L.chinensis+ K.integrifolia and Ph.communis + K.integrifolia community were taken respectively. Twenty Ph.communis plants in each Ph.communis, L.chinensis+Ph.communis and K.integrifolia +Ph.communis were sampled. The individual height, leave length, leave width, leave number and internode's distance of L.chinensis, Ph.communis were measured respectively, as well as the individual height, shoot length, shoot number and shoot distribution pattern (aggregation distribution, regular distribution) for K.integrifolia. The data on plant characteristics were analyzed by SPSS package. The experimental results showed that the module morphological characteristics of L. chinensis, Ph.communis and K.integrifolia changed under the interaction among coexisting plants and the hierarchy of competition for light were formed as followed: Ph.communis>K.integrifolia>L.chinensis. However, the hierarchy did not mean that  L.chinensis had disadvantage in competition. The trade-off between adaptation and interaction were achieved by the vegetative propagation. The plasticity of plant module morphological characteristics was affected by the light resource, the interactive relationship and the adaptation to environment.

 采用多样地随机取样方法,对不同群落中羊草、芦苇和全叶马兰的构件形态特征进行了观测与分析.实验结果表明:羊草、芦苇与全叶马兰在相互作用过程中,构件形态特征发生了不同程度的变化,并形成了光竞争等级:芦苇>全叶马兰>羊草.但是,光竞争的劣势地位并不表明羊草在种间竞争过程中处于劣势,羊草利用营养繁殖策略来达到适应性与种间关系的权衡.光资源、种间关系和植物的适应性都是影响植物形态可塑性的重要因子.

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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