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.
例如，“数据词典”和“数据模型”与元数据相关；而词汇表、控制词汇和分类法则与数据实例相关。
For example, "data dictionary" and "data model" are related to the metadata; whereas glossary, controlled vocabulary and taxonomy concern the data instances.
您可以认为它是一项在模型层完成的任务，因为要给数据分类。
You could argue it's a task to be accomplished in the Model layer, since you're categorizing the data.
物理数据模型的生成也保留术语表分类，准备好发布到元数据服务器。
Generation to physical data retains glossary classifications, ready for publication to metadata server.
生成的逻辑数据模型保留术语表分类。
Resulting logical data model retains glossary classifications.
业务角色和它们相关联的用例，显示了如何对分类模型数据进行报表？
Business actors and their associated use cases, which show how to report on grouped model data.
在顶级类别MDMCore Table . er1下面，您会看到MDM_CUSTOMER_SAMPLE逻辑数据模型的实体(现在是分类)和属性(现在是词条)，如图14所示。
Underneath top-level category MDM Core Table.ER1 you will find the entities (now Categories) and attributes (now Terms) of the MDM_CUSTOMER_SAMPLE logical data model, as shown in Figure 14.
这些错误表明在我们的模型中出了问题，我们的模型正在错误地分类某些数据。
These errors indicate we have problems in our model, as the model is incorrectly classifying some of the data.
我们了解了为了创建一个好的分类树模型，我们必须要有一个输出已知的现有数据集，从这个数据集才能构建我们的模型。
We learned that in order to create a good classification tree model, we need to have an existing data set with known output from which we can build our model.
此数据仅能在业务上下文中进行分类，而不能使用简单的层次结构模型（如第 3步中的模型）进行分类。
This data can only be classified within a business context and can't be classified using a simple hierarchical model, as shown in step 3.
物理数据模型的生成也保留术语表分类，准备好发布到元数据服务器。
Generation to physical data model also retains glossary classifications, ready for publication to metadata server.
这个模型可用于任何未知的数据实例，来预测这个未知数据实例是否通过只询问两个简单问题就能理解分类树。
This model can be used for any unknown data instance, and you are able to predict whether this unknown data instance will learn classification trees by asking them only two simple questions.
数据源、服务提供者和基础数据源模型之间的分类学和语义差异的数据映射和解析。
Data mapping and resolution of taxonomy and semantic differences between data sources, service providers, and underlying data source models.
新的数据没有分类别（这里是指还没有做过心脏病检查），评价过程根据挖掘模型将一个预测赋给每个新的记录。
The new data has no classification (in this case, no checks on heart disease have been made) and the scoring process assigns a prediction to each new record according to the mining model.
后端数据系统建立在一个语言环境模型的基础上-即一个根据“服务于全球化应用程序的语言环境信息”分类的数据系统。
The back-end data system is built on a locale model base — that is, a data system categorized by locale information that serves globalized applications.
设计分类法的方法是：查看内容并与领域专家进行会谈，从而创建一个适当的数据模型。
Taxonomies are designed by looking at content and talking to subject matter experts so that an appropriate model of the data can be created.
不过，由于模型的准确性很差，只能正确地分类 60 % 的数据记录，因此我们可以后退一步说：“哦，这个模型一点都不好。
However, because the accuracy of the model is so bad, only classifying 60 perent of the data records correctly, we could take a step back and say, "Wow. This model isn't very good at all.
我本想带您亲历用适合于分类模型的数据生成一个分类树的全过程。
I wanted to take you through the steps to producing a classification tree model with data that seems to be ideal for a classification model.
对于这类数据，分类树是一种极不适合的数据挖掘模型。
金融服务数据模型(Financial Services Data Model,FSDM)提供银行业务的概念和信息领域的分类模型。
Financial Services Data model (FSDM) provides a classification model for concepts and information domains in banking.
还利用三种飞机缩比模型的暗室测量数据，研究了时延神经网络分类器中时延单元数目对分类精度的影响以及分类器的分类性能。
The effect of time delay unit number on classification precision and the performance of TDNN classifier using three typical aircraft dark room data measured with scale model were studied.
一旦在部分类中像如下这样添加了的话，这些验证方法将会在应用程序中更新数据模型时自动的触发。
Once defined in partial classes like below, these validation methods will automatically be enforced anytime we write code to update our data model objects in an application.
本文把数据挖掘技术应用于基于客户价值矩阵的客户价值细分中，建立各类价值客户的分类模型。
This thesis applies data mining techniques to customer segmentation based on customer value matrix and builds the classification model of customer with different value.
在实际预测时，要对实时数据进行判别分类，选择对应的模型进行预测输出。
In actual forecasting, we must classify real-time data, choice the corresponding model to predict network output.
基于非平衡数据集的支持向量域分类模型，提出了一种银行客户个人信用预测方法。
A new predication method of customer credit of Banks is proposed based on the support vector domain classification model of non-balance data set.
在研究中，利用BP神经网络理论，提出并建立了按色相角范围对实验数据分类的打印机标定模型。
In the research, based on BP neural network theory, a printer calibration model is provided according to sorting experiment data by hue Angle range.
利用概念分类的聚类思想，使用概率和频率度量对行为数据分类，获得指导情报分析的模型。
Using the concept of classification of clustering, the behavior data were classified by probability and frequency, and the model was built for intelligence information analysis.
分类是一种重要的数据分析技术，可以用于提取描述重要数据类的模型和预测未来的数据趋势。
Sortation, which can be used to extract, describe major data type model and forecast the data tendency, is an important technology for data analysis.
摘要基于独特型免疫网络原理，提出了一种新型的分区记忆模式人工独特型网络模型，并利用其对卫星遥感数据进行了分类。
Based on idiotypic immune network theory, a Regional-memory-pattern Artificial idiotypic network (RAIN) is proposed to classify multi-spectral remote sensing image.
摘要基于独特型免疫网络原理，提出了一种新型的分区记忆模式人工独特型网络模型，并利用其对卫星遥感数据进行了分类。
Based on idiotypic immune network theory, a Regional-memory-pattern Artificial idiotypic network (RAIN) is proposed to classify multi-spectral remote sensing image.
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