特征向量

The new method is a combination of characteristic approximation to handle the convection part, to ensure the high stability of the method in approximating the sharp fronts and reduce the numerical diffusion, a smaller time truncation is gained at the same time, and a mixed finite elementspatial approximation to deal with the diffusion part, the sealer unknown and the adjoint vector function are approximated optimally and simultaneously.

此方法即为对方程的对流项沿流体流动的方向即特征方向进行离散，从而保证格式在流动锋线前沿逼近的高稳定性，消除了数值弥散现象，并得到了较小的时间截断误差；另一方面，对方程的扩散项采用混合元离散，可同时高精度逼近未知函数及其伴随向量函数，理论分析表明，此方法是稳定的，具有最优的L~2逼近精度。

Let V be an n-dimensional vector space over an algebraically closed field F of characteristic 0, where n = 2m.

设V是特征为0的代数闭域F上的n维向量空间，n=2m。

These above problems, which are very important and valuable in agricultural crops area monitoring, are currently less researched. Hence, in this paper, seven types of common texture and five vegetation indices were respectively added into TM multispectral bands to classify using three different methods, which are Minimum Distance, Maximum Likelihood and Support Vector Machine, and analyze the effect on winter wheat identification accuracy by comparing the classification results. The contexture include Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Variance, Angular Second Moment and Correlation, and the vegetation indices are RVI, SAVI, RDVI, NDWI and SLAVI.

为此，该文将平均值、方差、均一性、反差、相异性、熵、角二阶矩、灰度相关7种纹理信息以及比值植被指数、土壤调整植被指数、重归一化植被指数、植被液态水含量指数、有效叶面积植被指数5种植被指数信息分别加入到TM多光谱数据中，同时还进行了最佳波段选择，利用最小距离、最大似然和支持向量机3种方法进行分类提取小麦，研究了不同特征信息对小麦测量精度的影响。

We explored the single-trial estimation of P300 from channel Pz using support vector machine in three subjects, and gained a satisfied classification accuracy, which is 91.3%, 88.9% and 91.5% respectively. These results demonstrated the advantages of the inducing paradigm in constructing our mental speller.

本文利用支持向量机分类器对三名被试的脑电信号的载波成分进行了单次提取，特征数据来自通道Pz，以300ms~600ms时段的P300成分作为特征信号，对靶刺激的正确识别率分别为91.3%、88.9%和91.5%，证明了诱发模式的先进性，为系统的实现打下了基础。

First, a non-negative tensor factorization algorithm is improved by imposing sparseness constraints on it. Secondly, the bispectral images of mechanical signals are obtained and stacked to form a third-order tensor. Thirdly, the improved algorithm is used to extract features, which are represented by a series of basis images from this tensor. Finally, coefficients indicating these basis images' weights in constituting original bispectral images are calculated for fault classification.

首先，改进已有的非负张量分解算法，加入稀疏度控制策略；其次，将机械振动信号的双谱图像堆叠为一个三阶张量；然后利用改进后的分解算法对该张量进行二次故障特征提取，得到代表局部特征的"基图像"；最后，通过计算得出基图像在构成原双谱图像中所占的权重，并将得到的权重向量用于故障分类。

The SVM method is based on the theory of structural risk minoration. It can map the sample space to a higher feature space by selecting an optimal Kernel function, and get a linear regression in this higher feature space.

中文摘要：支持向量机方法是基于结构风险最小化原理提出的，通过采用合适的核函数将样本空间映射到一个高维特征空间，再在高维特征空间进行线性回归。

To find more suitable extraction algorithm for neural net recognizers, the applications of LPC sepstrum as well as the RW are investigated, and new procedures for the vector alignment and the normalization are proposed. The Time-Delay Neural Net model is generalized, and two kinds of non-uniformly windowed-time-windowed and component-windowed -pyramidical architectures are proposed.

为寻找比较适合于神经网络识别器的特征提取方法，文中研究了RW法和LPC倒谱系数特征提取法在神经网络中运用，并提出了一种新的时间对准和向量规整的方法；推广了时延神经网络模型，提出时间划分和分量划分两种加非均匀窗的金字塔状结构。

The proposed algorithm first detects smooth intervals based on gray variance from the original frame sequence, then concatenates them to constructs a new frame sequence. After that, video features such as pixel-wise difference, HSV histogram difference and edge histogram difference are fed to a SVM classifier to decide the types of different shot boundaries.

第一级分类器根据视频帧灰度方差特征，将无明显变化的视频序列从原始视频序列中分离出去，得到一个新的视频序列；第二级分类器在新视频序列的基础上，提取视频图像的像素对差值、HSV空间颜色直方图的各分量差值以及边缘直方图X，Y分量差值等视频特征，并采用支持向量机多分类策略进行镜头边界类型的检测。

In this thesis, based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier for predicting methylation status of CpG islands using a Support Vector Machine. Nucleotide sequence contents, transcription factor binding sties and Alu repeats are used as features for the classification. The method achieves accuracy of ~85% on the brain data.

本文的主要工作就是基于人脑测得的DNA甲基化数据，挖掘DNA序列特征，用支持向量机的方法实现了预测CpG岛甲基化的目标，得到了85%的预测正确率，同时还验证了所选的三类特征对于CpG岛甲基化的指导作用，它们分别是DNA序列组合、转录因子结合位点TFBS和短重复序列Alu。

This paper first extracts frames from digital video,locates and extracts oral area,then classifies oral area into lip and non-lip area by the Support Vector Machine.At last,based on knowledge about the structure of the mouth area to realize feature points location.

首先将视频文件分解为一帧帧的图片序列，定位并提取出图片中的口形区域，然后利用支持向量机将口形区域分为唇部和非唇部区域，最后根据口形图像几何特征的先验知识实现口形特征点的定位。

In the negative and interrogative forms, of course, this is identical to the non-emphatic forms.

。但是，在否定句或疑问句里，这种带有"do"的方法表达的效果却没有什么强调的意思。

Go down on one's knees;kneel down

屈膝跪下。。。下跪祈祷