• How should blank or missing values be handled in matching?

    在匹配中如何处理空白或缺失的值?

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  • When XML node values are compared, a question arises: How should missing values be evaluated?

    比较xml节点值时,问题出现了:缺少的值应该如何被评估?

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  • When you press Enter at this point, you will be prompted for the missing values that are required.

    如果在这里按Enter,会提示输入缺少的必需值。

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  • Filling missing values, smoothing noise data and removing inconsistent data were all adopted to get high quality data.

    通过补全缺失数据、平滑噪声数据、消除不一致数据等技术,得到高质量的数据。

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  • A imputation method based on Mahalanobis distance was proposed to estimate missing values in the gene expression data.

    提出了一种基于马氏距离的填充算法来估计基因表达数据集中的缺失数据。

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  • RESULTS: The multiple imputation method imputed missing values of the crossover design and generated valid statistical inferences.

    结果:多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。

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  • Results The multiple imputation method can impute missing values of the crossover design and generate valid statistical inferences.

    结果多重填补的方法可用于交叉设计中缺失数据的填补并得出正确的统计推断。

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  • We resort to expectation maximization (EM) algorithm for both the estimation of model parameters and the coping with missing values.

    这里,期望最大化算法既用来处理丢失值又用来估计模型参数。

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  • The attribute missing values filling, attribute reduction and the choice of decision tree branch attributes are researched in this paper.

    本文从属性值缺失的填补、属性约简和决策树分支属性选择三方面进行研究。

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  • Compared with increasing experiments, missing value estimating is preferred in reducing the influence of missing values on the post-processing.

    在不增加实验次数的情况下,缺失值估计是降低缺失数据对后续分析影响的有效方法。

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  • Some key aspects about algorithm are introduced here, such as how to deal with high-branching and numeric attributes, missing values as well as how to prune.

    介绍了算法中如何处理高分枝属性、数值属性和缺失数据及剪枝等关键环节。

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  • The way of effective data complement for a data set with missing values was analyzed so as to reflect more objectively internal relationship among data in data set.

    分析了在含有遗失值的数据集上如何进行有效的数据填补,以便更客观地反映数据集中数据所隐含的内在联系。

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  • The data with missing values in two-factor experiment with randomized block design is the unbalanced data with unequal replications of level combination of two factors.

    有缺失数据两因素随机区组试验资料是两因素水平组合重复数不等的非平衡资料。

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  • Objective to compare the three imputation methods of missing values and provide scientific basis for the best imputation methods of missing values for the schistosomiasis surveillance data in China.

    目的以全国血吸虫病疫情监测资料为数据来源,比较不同缺失值处理方法对模拟缺失值的处理结果,为确定适用于处理该资料缺失值的方法提供依据。

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  • Listing 17. Missing values in conssl.cfg file.

    清单17.conssl . cfg文件中缺少值。

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  • Missing values contained in microarray data will affect subsequent analysis.

    微阵列数据中的缺失值会对随后的数据分析造成影响。

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  • Missing values in traffic flow data should be imputed because complete data are needed for space-time data mining.

    交通流量的时空数据挖掘需要完整的数据,因此必须处理交通流量数据中的缺失值。

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  • Missing values in traffic flow data should be imputed because complete data are needed for space-time data mining.

    交通流量的时空数据挖掘需要完整的数据,因此必须处理交通流量数据中的缺失值。

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