Listing 17. Missing values in conssl.cfg file.
清单17.conssl . cfg文件中缺少值。
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？
Missing values for keystore and stash file location in conssl.cfg file
cfg文件中缺少密钥库的值和存储文件位置
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。
They spoke about critically missing values, like community, equity, and social justice.
他们还重申了极为欠缺的集体、公正和社会正义等观念。
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？
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。
Conclusion mi and MIANALYZE procedures provide a valid strategy for handling data set with missing values.
结论多重填补与多重填补分析为处理存在缺失数据的资料提供了有效的策略。
As with a normal aggregate initializer, missing values are treated as though they'd been given 0 as an initializer.
使用普通的聚合初始化程序时，缺少的值会被认为它们已经被初始化程序指定为0来处理。
The usual procedures for the estimation of missing values are the ones based on the principle of least squares.
实验工作者时而发现某些实验数据缺落而需要另行估计。通常用最小二乘法。
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。
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。
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。
When we analyse seasonal series of hospital data, there are missing values and outliers. It is difficulty for forecasting.
医院季节性时间序列分析中，会出现缺失数据和异常值，这就影响了预测预报。
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。
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。
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。
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。
Conclusion The multiple-imputation method was the best technique to handle with the missing values in the schistosomiasis surveillance data.
结论多重填充技术较为适合处理该资料中缺失比例较少的缺失值。
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。
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。
You control the data collection process, so you can ensure data quality, minimize the number of missing values, and assess the reliability of your instruments.
你控制着数据收集过程，因此您可以确保数据质量，将丢失的数量价值减到最小化，评估仪器的可靠性。
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。
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。
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。
Perhaps then you will need to validate parameter values, or assign defaults to missing parameters.
接下来您可能需要验证参数值，或者为丢失的参数指派默认值。
The map supplied when creating a virtual system must provide values for any missing properties and parameters, and can additionally supply values to override the default values.
在创建虚拟系统时给出的映射必须为任何缺失的属性和参数提供值，并能另外提供值以覆盖默认值。
Missing statistics: When statistics are missing, the optimizer assumes the default values to determine costs, which could be completely inaccurate.
统计数据丢失：当统计数据丢失后，优化器假设一个默认值来确定成本，这非常不准。
If one or more of these files is missing, or even if some of the configurations are changed to incorrect values by mistake, the migration process fails.
如果一个或多个文件丢失，甚至配置设置错误，迁移过程就会失败。
Values can be propagated across linked records for missing or conflicting data so that a common representation of name or tax ID will be present on all linked records.
对于缺失或有冲突的数据，值可以在链接的记录之间传播，以便在所有链接的记录中提供名称或taxID的通用表示。
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