分析了多项式曲线与样本点的误差。
The errors between the polynomial curve and samples order were analyzed.
使用两个样本点，计算计数器的效能资料。
Calculates the performance data of the counter, using two sample points.
重复这一步骤直到全部样本点被合理分类为止。
This procedure is repeated until one gets the reasonable classes for all points.
当样本点达到一定数目时，计算结果的稳定性好。
While the sample points reach to a certain number, the stability of calculation result is good.
当样本点达到一定数目时，计算结果的稳定性好；
When the number of sample data points is big enough, the calculating result is rather stable;
该方法解决了分类过程中样本点分散和样本不可分问题。
The new method can resolve sample dispersion and the unclassifiable region problems.
这些样本点的求得能有效减少求解最小聚类的时间复杂度。
By working out such sample data the time complexity of figuring out min cluster is effectively reduced.
针对变量多重相关性及解释变量多于样本点等实际问题，伍德S。
For the problem of multicollinearity and the number of explanatory variables rather than practical issues, s.
均值移位算法是一种搜索与样本点分布最相近模式的非参数统计方法。
The mean shift algorithm is a nonparametric statistical method for seeking the nearest mode of a point sample distribution.
进而通过轨迹样本点分析的方式，得到统计平均的相邻平行截面间距。
Then the statistical average distance between neighboring parallel sections was acquired using the analysis of sample trace points.
该方法以样本点距离失效空间和可靠空间的距离之差作为样本点的输出变量。
The new method takes the difference between the distance from sampling point to failure space and the distance from sampling point to reliable space as the output variable.
回归软件程序将样本点用图表示，并且给出最拟合样本点的直线的价值函数公式。
Regression software programs graph the sample points and give the (cost-function) formula of the straight line that best fits the points.
采用特征滤波器来提取样本集中具有线性特征的点，同时生产样本点之间的相互关系；
A feature filter is presented to extract linear points and their relations from input samples.
首先通过一系列样本点有限元试验结果建立目标函数和设计变量的神经网络响应面模型。
A response surface using artificial neural network is formed on the ground of initial finite element experiments.
该方法在样本点相同的情况下减小了近似模型的推广误差，提高了近似精度，增加了适应性。
By using this method the extended error of the approximation model is diminished, the approximation precision is improved, and the flexibility is enhanced based on having the same sample points.
推广后的定位方法，可根据具体的目标定位场合，灵活选择核函数对样本点进行核密度估计。
Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
结果表明，经选择后的新特征较好地描述了样本点的物理性质和发震危险点与安全点的差异。
As a result, the new feature after selection describes better the physical properties of the pattern points and the difference between safe points and dangerous points of earthquake occurrence.
基于非线性多项式方程的零点配对算法以及临界点算法，给出了一种求平面代数剖分样本点的改进算法。
Based on the critical point algorithm and zero-match algorithm, an improved algorithm for finding sample points of algebraic decomposition was proposed.
通过引入“样本域”的概念，由所给的有限个样本建立最大相似于样本点的样本域，计算被测样本的相似度。
By the idea of "stylebook domain", a domain that contains all stylebooks was created, to calculate the Similarity of the tested stylebook.
最后以另一个大的比例减去位于距异类中心较远的对分类不起作用的样本点，以便提取具有代表性的边界向量。
Finally, the other large proportion is decided to reduce those sample points lie on the further from the different class center so that the representative boundary vectors can be extracted.
神经网络的理论基础是最小化经验误差，这种基于传统的渐进理论的学习方法，在训练样本点无穷多时是适用的。
Because neural network is based upon empirical risk minimization and asymptotic theories, it is suitable to deal with situations where the amount of samples is tremendous and even infinite.
由于该方法中判断某个决策单元是否DEA有效，是以各实际样本点的外包络面为基础的，因此称为数据包络分析。
In this method to tell whether one DMU is DEA efficient or not is based on the frontier of real sample points, so it is called Data Envelopment Analysis.
通过减变量残差图，不但可以容易地考察一个变量在模型中的作用，而且可以诊断样本点对模型和变量的影响大小。
It can not only be used to determine the importance of an explanatory variable in the regression model, but also to detect some cases with influence on the model and variables.
此方法允许在设定了时域样本点数后随意调整频域样本，点的数量和位置，使滤波器的设计变得十分直观、灵活、简单。
The amount of these sampling data and frequence positions of these sampling data be flexibly adjusted, and the design is easy, simple, and flexible.
基于这一问题本文通过构造统计量对所给的样本点进行选择，剔除对模型的构造有很大影响力的样本，从而获得一个相对合理的样本空间。
Based on this problem, this article selects the sample points by constructing statistics. First, it removes the outliers to have a relatively reasonable sample space.
在建立代理模型时，通过连续成批地在设计空间的全局和局部均加入新样本点，不断提高代理模型的全局拟合精度，直至获得满意的代理模型为止。
By sequentially adding sampling points both globally and locally in the design space, the accuracy of the surrogate models is gradually improved until reaching the expected level of accuracy.
在建立代理模型时，通过连续成批地在设计空间的全局和局部均加入新样本点，不断提高代理模型的全局拟合精度，直至获得满意的代理模型为止。
By sequentially adding sampling points both globally and locally in the design space, the accuracy of the surrogate models is gradually improved until reaching the expected level of accuracy.
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