样本

First we construct a covariance matrix from sample images, then compute the eigenvalues and corresponding eigenvectors of the covariance matrix, construct a feature matrix with the eigenvectors. Then every images in database can be projected into the feature matrix and gain a projection vector, so does the input image. Then we can judge the resemblance between input image with each image in database by computing the distance between their projection vectors.

我们首先根据采集的样本图像构造一个协方差矩阵，然后求取该矩阵的特征值，以这些矩阵特征值对应的特征向量构造出一个特征空间，然后将输入图像向该特征空间映射，将获取的映射系数与样本库中图像的映射系数进行距离计算，根据计算出的距离判定输入图像与样本图像间的匹配程度。

Five ways on comparing covariance matrix are applied to the Shanghai 50 Indexes Stock Exchange, which are sample covariance matrix, scalar matrix, two-parameter covariance matrix, single index matrix, constant correlation matrix. We adopt principal components method and Markowitz portfolio method to measure stock market risk using VaR, getting the effect of measuring market risk. The result shows that sample covariance matrix and two-parameter covariance matrix could measure market risk more effectively.

本文以上证50指数数据为样本，采用样本协方差矩阵、数量矩阵、两参数模型矩阵、单指数模型矩阵、常量相关矩阵作为与股票相关的协方差矩阵，结合投资策略选择的主成分方法和Markowitz最优投资组合方法，计算VaR以度量市场风险，并比较了五种协方差矩阵度量市场风险的效果，结果表明，在主成分方法中，样本协方差矩阵和两参数矩阵方法能有效的度量市场风险。

What is the probability that sample mean will be with 1 unit of ?

假定某样本的方差是10000，如果那出一个样本是64，那么该样本的期望在一单位的是多少？

What is the probability that sample mean will be with 1 unit of M?

假定某样本的方差是10000，如果那出一个样本是64，那么该样本的期望在一单位的M是多少？

In contrast, the proposed algorithm based on space block and sample density is fast. It is applied in intrusion detection in this paper.

该算法根据样本的局部密度选择训练样本，减少参加训练的样本数量，提高学习速度。

This definition limits the size of sample space,but can't represent all the features of non-face.It's demonstrated by this definition that the difference between face and

这一定义虽然限制非人脸样本空间的大小，但是定义的非人脸并不能表征所有的非人脸样本}fu只能是特征样本。

To the problem that large-scale labeled samples isn't easy to acquire in the course of SVM training, the active learning strategy is used in the SVM training and an incremental training algorithm of active SVM based on the uncertainty based sampling of distance ratio is proposed in the paper. The experimental results show that the active SVM learning strategy can considerably reduce the labeled samples and costs compared to the passive learning method, and at the same time it can ensure the accurate classification performance is kept as the passive SVM and also expedite the SVM training.

针对 SVM训练学习过程中难以获得大量带有类标注样本的问题，本文将主动学习策略应用于SVM增量训练中，提出了一种基于距离比值不确定性抽样的主动SVM增量训练算法，实验结果表明在保证不影响分类精度的情况下，应用主动学习策略的SVM选择的标记样本数量大大低于随机选择的标记样本数量，大大降低了标记的工作量或代价，而且训练速度同样有所提高。

Analytic methods based on elementary parametric and non-parametric models for one sample; two sample, stratified sample, and simple

主要目标是由已知的样本资料经由正确有效的分析来推测母体所具有的特性；另外则是适当地设计样本取得的程序及取样的范围，以便使所观察样本显现的特性能真实反映母体的现象，进而能以简洁的分析，作有效的推论。

Proposing to generate more samples by subsampling to deal withthe problem that RBMNN needs a great deal of data for training.

针对RBMNN需要大量样本进行训练的缺陷，提出了通过低采样由单个样本生成多个样本的思路。

The traditional mean shift algorithm requires a symmetric kernel, such as a circle or a rectangle, and assumes that the target's scale is symmetric during the course of tracking. The pixels are used on the edge as the samples, about 5% to 10% of the total pixels in the kernel.

与采用对称核函数的均值迁移目标跟踪算法相比，采用目标边缘点作为核函数中的样本点，参与计算的样本点为核窗口中样本的5%~10%，使图像处理速度达到了50帧/s以上，满足了实时跟踪的要求。

The concept of equivalent rotationally rigidity is offered and the formula of rotationally rigidity is obtained.

主要做了如下几个方面的工作：对伸臂位于顶部的单层框架—筒体模型进行分析，提出了等效转动约束的概念和转动约束刚度的表达式。

Male cats normally do not need aftercare with the exception of the night after the anesthetic.

男猫通常不需要善后除了晚上的麻醉。