本文给出了一个模糊多元数据的假设检验方法。
Statistical Hypotheses Testing Method for Fuzzy Multivariate Data;
多元数据可视化是理解多元数据的一种重要手段。
Multivariate data visualization is an important way of understand multivariate data.
多元数据的平行坐标是信息可视化的主流技术之一。
Parallel coordinates is one of the mainstream technology of information visualization.
数据概要旨在更好的理解信息并创建关于对象的更多元数据。
Data profiling aims to understand information better and create additional metadata about objects.
MLSE采用层机制集成多元数据，实现场景绘制和交互。
By means of scene layer, MLSE enables multi variates integration, scene rendering and user interaction.
提出了一种多元数据的点得分平行坐标表示及可视化分析方法。
A new multivariate data visualization method named point-score parallel coordinates (PSPC) is proposed in this paper.
并针对多元数据数据量大，操作复杂的特点，为其进行存储设计。
Particular physical storage design for multimedia and spatial data is performed based on their characteristics, including mass data volume and complicated operations.
多元数据集遭受着表征的问题，因为三维以上超越了绘图程序的能力。
Multivariate data sets suffer from the problem of representation, since a dimensionality above 3 is beyond the capability of plotting programs.
模式识别；可视化；多元数据；图表示；几何代数；子空间坐标；优化。
Pattern Recognition; Visualization; Multivariate Data; Graphical Representation; Geometric Algebra; Subspace Coordinates; Optimization.
在一个简单投影指标下，用新的优化途径建立了多元数据分类模型，并将其用于多指标的标准水质分类。
Secondly, a new projection index based on dynamic cluster rule is constructed in the PPDC model, which would finish the sample clustering based on the projected characteristic value.
到目前为止，最重要的原因为执行一个主成分分析(PCA)是使用它当作一个工具，用于筛选的多元数据。
By far, the most important reason for performing a principal components analysis (PCA) is to use it as a tool for screening multivariate data.
本文提出多元数据的子空间坐标图表示模型，该模型可以将这些传统多元图表示方法统一到同一个表示框架。
A subspace graphical representation model of multivariate data is proposed, which unites several traditional multivariate data visualization methods into the same representation framework.
首先从多元数据分析的角度，研究了基于相关性的多元数据分析和基于独立性的多元数据分析的特征融合方法。
We study the algorithms of feature-level fusion based on the viewpoint of dependence and independence multi-variant data analysis, respectively.
除了减少另外的系统调用之外，这种不可移植的扩展还减少了从文件系统获取更多元数据的开销非常高的访问操作。
Besides saving you another system call, this non-portable extension saves you an expensive trip back to the file system for more metadata.
实际上，请注意到所需要的很多元数据元素，和Friendofa Friend (FOAF)词汇表中的元素都是相同的，因此我们建立了清单1中所显示的格式。
Instead, noting that many of the metadata elements required are common with those from the Friend of a Friend (FOAF) vocabulary, we created the format shown in Listing 1.
仅因为数据是多元的并不意味着就必须使用多元工具研究它。
Just because your data is multivariate does not mean you only have to examine it with multivariate tools.
它由许多元素组成，这些元素在文件系统和物理数据中跟踪条目之间的关系(比如文件名)。
It consists of a number of elements that track the relationship of the entry to other entries in the file system as well as physical data (such as the file name).
很多医生都建议，应该在加工食品的标签上添加相关的胆固醇、饱和脂肪和多元化不饱和脂肪的含量数据。
Many doctors believe that the labels on processed food should spell out the amounts of cholesterol, saturated fat and polyunsaturated fat the food contains.
想想你们的非盈利组织，以上数据貌似不能证明多元化的好处。
Think about your Nonprofit organization – chances are it’s not a paragon of diversity.
韩国和日本的数据则更能说明中国外汇储备的多元化，不过相对中国总外汇储备而言涉及的金额较小。
Data from South Korea and Japan paint a somewhat more illuminating picture of Chinese diversification, though the amounts involved are small relative to China's total holdings.
然而这一受到广泛引用的数据是多元化方面的一个误导性指标。
But this widely cited number is a misleading indicator of diversity.
数据分析采用方差分析、多元回归分析。
Data were analyzed using variance and multiple regression analysis.
URI在 mddl.org域中；mddl.org是维护市场数据定义语言（Market Data Definition Language）这一XML词汇表的组织，custodian 是该词汇表中众多元素中的一个。
The URI is in the mddl.org domain; mddl.org is the organization that maintains the Market Data Definition Language, an XML vocabulary in which custodian is one of many elements.
数据分析主要有多元回归分析等。
采用主成分回归（PCR）和偏最小二乘法（PLS）对试验数据进行了多元统计分析。
Then all data were analyzed by principal component regression (PCR) and partial least square (PLS).
数据间相关性采用多元线性逐步回归检验。
The correlation among data was tested with multiple linear stepwise regression.
数据间相关性采用多元线性逐步回归检验。
The correlation among data was tested with multiple linear stepwise regression.
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