The following questions resemble a sample set.
以下问题类似于一个样本集。
A sample set-up screen is shown in Figure 2.
图2显示了一个样例设置屏幕。
A sample set of failed events are generated before migration.
在迁移之前，生成一组失败的事件。
A sample set of failed events are generated before the migration.
在迁移之前，生成一组失败的事件。
Listing 1 shows a sample set of CVS commands along with short descriptions of each.
清单1给出了一组CVS命令示例和简短的相关描述。
The following is a sample set of messages that are displayed after the application has completed.
下面是应用程序完成之后显示的消息示例。
The method was test through real sample set, and 97.47% of analysis precision rate was attained.
经实际样张测试，本文方法的分析精度达到了97.47%。
Before detecting the fault, the sample set is used to train the neural net work by LMBP algorithm.
在故障检测之前，需要利用样本集通过反向传播算法(即lmbp算法)来训练神经网络。
Figure 2 shows a sample set of three raw devices that contain both free chunks and chunks that are in use.
图2显示包含3个原始设备的一组原始设备，这3个原始设备都既包括空闲块，又包含正在使用的块。
The technical key of the method is to create the sample set and initial weight as well as to optimize the model.
该方法的技术关键是样本集和初始权重的建立，以及模型的优选。
Training sample set inevitably contains gross error in input signal reconstruction of nonlinear multifunctional sensor.
在非线性多功能传感器的信号重构过程中，训练样本集不可避免地夹杂粗差数据。
To test this system, I'm using a sample set of test results in three different XML files: test1.xml, test2.xml, and test3.xml.
为了测试该系统，我使用了三个不同的XML文件作为测试示例：test1.xml、test2.xml和test3.xml。
A data fusion algorithm for sample set transforms surface problem into a curve fitting problem and a curve interpolation problem.
该算法将曲面问题转化为一个曲线拟合问题和一个曲线插值问题。
Based on the setting up o failure mode's testing model, the adequacy measurement and criterion of test cover for the sample set are proposed.
在建立故障模式测试模型的基础上，提出了样本集的测试覆盖充分性度量和准则。
While you might want to hand-roll your own code for really large amounts of image processing, NetPBM serves as an excellent sample set to test with.
尽管您可能会希望使用自己的代码来进行大量图像处理过程，不过NetPBM的确可以用作一个非常好的用于测试的样本集。
The results show that it has better performance than the other three classifier on the standard text sample set, and it has some superiority...
结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器，在小样本分类上具有一定优势。
Example 30. To control the sample set on which the statistics will be collected and to be able to repeatedly use the same sample set, use the following.
为了控制收集统计信息的样本集，以及可以重复使用相同的样本集，需要使用下列语句。
Support vector machine (SVM) based on the structural risk minimization of statistical learning theory is a method of machine learning for small sample set.
基于统计学习理论中结构风险最小化原则的支持向量机是易于小样本的机器学习方法。
By researching cored samples, 9 logging parameters reflecting distinctly lithology are selected by cross plot technique, and build up lithologic sample set.
首先依据岩心取样进行岩性归类，然后通过交会图技术确定了9个对岩性反映较明显的参数，并建立起研究区域的岩性样本集。
First, the time-drift of sensor's characteristic and its change law with temperature are obtained by the accelerated test, then the sample set is established.
首先，通过加速试验获得了传感器特性的时漂以及受温度影响的变化规律，并据此构建了样本集。
Firstly, sample set is roughly classified using ART to reduce the scale of samples, in training set, and then all small training sets is trained using parallel BP.
首先用ART网络对训练集中的样本进行粗分类，以减小训练集的样本规模，然后用多个BP网络并行地对小训练集进行训练。
The results show that it has better performance than the other three classifier on the standard text sample set, and it has some superiority on small set of samples.
结果表明该分类器在标准文本样本集合上的性能好于其他三种分类器，在小样本分类上具有一定优势。
First using sample estimate method to find the search keywords, and then analyzing the words 'frequency in the sample set to get the formula of the sample frequency.
采用样本估计的方法产生候选关键词，并对样本词频进行了进一步的分析，得出了样本词频公式。
An evaluation method fur failure sample set based on fuzzy grey relation analysis is proposed in order to obtain perfect failure sample set before testability demonstration experiment.
为了在测试性验证试验之前获得理想的故障样本集，提出了一种基于模糊灰色关联分析的故障样本评估方法。
After the essential pretreatment and reasonable choice of the logging data have been done, a correct sample set may be established by combining the logging data with geological parameters.
对测井资料进行必要的预处理和合理的取舍后，与地质参数结合，建立起正确的样本集。
The kernel based weighted KNN algorithm solves the multi peak distribution problem and the overlap boundary problem of the sample set, as well as the classifier's precise decision problem.
基于核的距离加权KNN算法解决了样本的多峰分布、边界重叠问题和分类器的精确分类决策问题。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
And based on the experimental results of multi-dimensional data clustering, anomaly detection matrix is determined through identifying the training sample set and the machine self-learning.
然后根据对多维数据聚类的实验分析结果，通过对样本集的训练进行标识和机器自学习过程来判别异常检测矩阵。
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