特征向量

The representative points of positive case support vectors and negative case support vectors in sample set are defined, the measure and eigenfunction between sample x and y allusiveness from initial space to eigen space under a kernel function k are researched, and a correlative theorem with demonstration is given.

定义了样本集中的正例支持向量和反例支持向量的代表点，研究了原始空间样本x，y在核函数k映射下在特征空间中的x，y映象之间的度量和特征函数并给出了相关的定理及证明。

Data sample pretreatment of decision tree and Support Vector Machine method is conducted by the documents of product design knowledge segmentation processing, feature extraction, feature merger and expression vector.

通过对产品设计知识文档的分词处理、特征抽取、特征合并及向量表达，为决策支持向量机方法提(来源：d6f5A6fBC论文网www.abclunwen.com)供样本数据预处理。

In the matching stage, the angle of feature vectors is used as the similarity measure in the high dimensional space directly. This similarity measure is invariant to the linear transformation of feature vectors. Compared with SIFT and normalized correlation, the proposed method needs no primary features such as edges, corners, gradient or shapes which are very unstable in low contrast and low NSR images.

在匹配过程中，以向量之间夹角的大小作为相似度度量，直接利用图像的灰度信息在高维向量空间中考虑图像之间的相似度，克服了在低照度、低信噪比的图像中求边缘、角点、梯度和形状等特征的困难，不受向量模大小以及向量平移的影响，即相似度的计算是线性光照不变的。

However, in regression model if there are features that are not completely relative or even completely irrelative to the problem, the difference of features' correlative degree to the problem becomes large and it may affect the performance of support vector regression machine.

基于统计学习理论的支持向量回归机有比较好的泛化能力，然而当样本含有与该问题不完全相关甚至完全无关的特征时，会使得各个特征对问题的相关程度差异很大，从而使得支持向量回归机的效果受到影响。

A new similarity measure based on concept semantic space is designed.

本方法的优点是：在计算两个向量之间的距离时，即使某个特征不存在，但与其具有语义关联的其它特征可以在某种程度上起替代作用，从而较为精确的计算了向量间的距离。

Based on the intrinsic nullspace property of the SVM proven by the authors, which shows that the normal vector of the SVM decision plane is of the nullspace property in terms of the support vectors-based within-class scatter matrix, a support vectors-based method is presented to construct the Kernelized Decision Boundary Feature Matrix by using the SVM normal vectors.

进一步结合均值向量的差向量构建扩展决策边界特征矩阵。最后以支持向量为训练集合，结合零空间方法来计算投影空间，该投影空间被用来从原始图像中提取判别特征。

The design of vehicle type classification based on Support Vector Machine is collecting characteristic parameters provided to segregator after vehicle image preprocessing and feature extraction,and classifying the type of vehicle by using technique of Support Vector Machine and binary decision tree according to vehicle image from video camera.

基于支持向量机的车型分类的设计思路是通过视频采集获得车辆图像，对车辆图像进行图像预处理和特征提取后，得到分类器所需数据特征，而后采用支持向量机和二元决策树对车型分类。

Information entropy, coincidence degree and its standard errors of axle centre trail quadrant area vector under different bearing states are extracted, the sensibility degree of the different parameters for the bearing states in different quadrant division is analyzed. By modifying the parameter compute method, the sensibility degree is improved.

提取各典型状态下轴承轴心轨迹象限面积向量的信息熵，和多周期面积向量的重合度以及重合度标准差，分析在不同象限划分下，各特征参数对状态的敏感程度，通过改进特征参数计算方法，提高对状态的灵敏度。

The method firstly organizes support vectors in clusters in feature space, and then,it finds the pre-images of the cluster centroids in feature space to construct a reduced vector set.

此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。

The whole process of text representation were discussed — word segmentation, building stop words list, feature selection, weight computation and generating vector space. Aim at the influence of stop words, a list, which is fit for text categorization, is set up and makes the vector dimension reduce.

针对停用词对分类的影响，建立了适合文本分类的停用词表，使向量维数得到初步降低；对现有特征选择方法进行了介绍和对比，构造了一种适合支持向量机的特征选择函数——基于类内频率的特征选择函数。

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