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最大分类
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  maximum classification
     A Cluster Validity Function Based on Maximum Classification Information
     基于样本最大分类信息的聚类有效性函数
短句来源
     Based on the maximum classification information, a new cluster validity function, which modified Bezdek's partition coefficient, is defined.
     根据每一样本的最大分类信息 ,对 Bezdek定义的划分系数进行修改 ,提出了一个新的聚类有效性函数 ,讨论了该函数的若干性质。
短句来源
     Bezdek's partition coefficient,improved partition coefficient based on maximum classification information and two kinds of normalized partition coefficients are experimented and analyzed ,the conclusion shows that classification performance of two kinds of normalized partition coefficient is improved markedly.
     Bezdek提出的划分系数 ,基于样本最大分类信息的改进划分系数 ,以及二者的归一化形式的分类性能进行了实验分析 ,结果表明 ,归一化处理的分类性能有明显提高。
短句来源
     Some properties of modified partition entropy, which based on pattern's maximum classification information are discussed. Compare experiments about Bezdek's partition entropy, modified partition entropy which based on pattern's maximum classification information and normalized partition entropy are given.
     讨论了基于样本最大分类信息的改进划分熵的若干性质 ,并对Bezdek提出的划分熵、基于样本最大分类信息的改进划分熵、划分熵的归一化形式的分类性能进行了比较实验。
短句来源
  “最大分类”译为未确定词的双语例句
     The modified partition coefficient based on maximum membership degree information only uses the fuzzy classification information of the data set. In order to consider the geometry structure information of the data set, a new clustering validity function is presented using the maximum membership degree information combined with maximum inner variance of the data classification set.
     基于样本最大分类信息的改进划分系数仅仅利用了数据的模糊分类信息 ,为了将数据分类的几何结构信息考虑进去 ,结合数据分类的最大类内变差 ,提出一个聚类有效性标准 .
短句来源
     An efficient decision-forest based approach was developed for feature gene extraction, and it can fulfil multiple gene mining tasks, such as mining disease genes or classification of biological types.
     基于决策树的基因芯片数据分析方法以追求样本最大分类正确率为目标,造成大量的部分相关基因被排除,不适用于挖掘复杂疾病的相关基因. 为此,提出了特征基因挖掘的决策森林方法:从多组特征子集中识别重要疾病相关基因,每个子集根据自身对目标的分类能力被识别;
短句来源
     RESULTS: The proposed method was applied to the test dataset with 140 trails. 1 152 sample EEG were analyzed with 1 second (128 sample points) sliding windows stepped by four sample data. The satisfactory results were obtained with the highest classification accuracy 79.28%.
     结果:对140次实验的测试样本数据,采用长度为1s(128个采样点)间隔为4个采样点的滑动窗对1152个采样点进行分析,最大分类正确率可达79.28%。
短句来源
     An approach based on multi-objective genetic algorithm is proposed to construct fuzzy classification system with maximum precision and minimum number of features and minimum number of fuzzy rules.
     提出了一种基于多目标遗传算法的模糊建模方法,实现了具备最大分类精度、最少特征变量和模糊规则数目的模糊分类系统的设计.
短句来源
     It is heat point that the technology of support vector machines is used to solve problems of pattern recognition. Posterior probability of simples is important in pattern recognition. But Standard support vector machines do not provide posterior probability.
     目前支持向量机解决模式识别问题是广大学者研究的热点,样本的后验概率在模式识别中至关重要,但是传统的支持向量机技术不提供后验概率.本文针对这一问题进行了三个方面的研究:1)在给出样本点后验概率的基础上,将大规模优化问题分解成最大似然函数和最大分类边界两个小规模优化问题;
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  maximum classification
The results using a tenfold cross validation scheme gave a maximum classification of 97.33% when tested on 800 unseen LGB EEG features from 40 subjects.
      
A maximum classification accuracy of 93.75% was achieved and the result shows a performance comparable to previous studies using either coarse features or the finer features called minutiae.
      
A simple feature selection process increased the maximum classification rate to 98.6 and resulted in much more reproducible results.
      
It could be combined later to reach the maximum classification accuracy as was done in this research.
      
In general, classifiers improve their performance with dataset size, but this performance is asymptotic to the maximum classification accuracy.
      
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The modified partition coefficient based on maximum membership degree information only uses the fuzzy classification information of the data set. In order to consider the geometry structure information of the data set, a new clustering validity function is presented using the maximum membership degree information combined with maximum inner variance of the data classification set. Experiment results show that the new method has good classification performance.

基于样本最大分类信息的改进划分系数仅仅利用了数据的模糊分类信息 ,为了将数据分类的几何结构信息考虑进去 ,结合数据分类的最大类内变差 ,提出一个聚类有效性标准 .实验结果表明 ,该方法具有良好的分类性能

Based on the maximum classification information, a new cluster validity function, which modified Bezdek's partition coefficient, is defined. Some properties are studied and comparison experiments shown that this function is superior to partition coefficient in some cases.

根据每一样本的最大分类信息 ,对 Bezdek定义的划分系数进行修改 ,提出了一个新的聚类有效性函数 ,讨论了该函数的若干性质。实验结果表明在某些情形该函数的判决效果优于划分系数。

In this paper,classification performances of J.C.Bezdek's partition coefficient,improved partition coefficient based on maximum classification information and two kinds of normalized partition coefficients are experimented and analyzed ,the conclusion shows that classification performance of two kinds of normalized partition coefficient is improved markedly.

对J C .Bezdek提出的划分系数 ,基于样本最大分类信息的改进划分系数 ,以及二者的归一化形式的分类性能进行了实验分析 ,结果表明 ,归一化处理的分类性能有明显提高。

 
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