几何距离的英文

• geometrical distance

• 几何    how much; how many
• 距离    distance; range; gap; space; ...
• 平均几何距离    mean geometrical distance
• 几何距离模式几何距离模式    geometric distance model
• 几何分辨距离    geometric resolution distance
• 几何平均距离    mean geometrical distance
• 几何    1.[书面语] (多少) how much; how many 不知尚有几何 uncertain how much is left or how many are left2.(几何学) geometry; 几何比 geometric proportion; 几何分布 geometric distribution; 几何光学 geometrical optics; 几何关系 geometrical relationship; 几何级数 [数学] geometric [geometrical] progression; geometric series; 几何图形 [数学] geometric figure
• 距离    1.(相隔的长度) distance; range; gap; space; spacing; separation 保持一定距离 keep a certain distance; 扩大距离 increase the distance; widen the gap; 两座房屋之间的距离 the distance between the two houses; 缩小距离 reduce the distance; lessen the gap; 蝙蝠用一种回声测定法来判断距离。 the bat judges distances by a kind of echo-location. 然而他们(在思想感情上)与我们仍有距离。 they, however, maintained a distance from us.2.(相距) be apart from; be away from; be at a distance from 距离约 300公里 be about 300 kilometres apart from each other; 这学校距离车站三英里。 the school is three miles distant from the station.; 距离变换 range conversion; 距离标识器 range marker; 距离标志 [工业] distance marker; radar range marker; range marker; 距离标志灯 [航空] distance marking light; 距离表 distance table; 距离测定器 map ranging device; 距离测量 [军事] range measurement; 距离测量精度 range measurement accuracy; 距离传感器 range sensor; 距离方位显示器 range-azimuth indicator; range-bearing display; 距离分辨力 range resolution; range discrimination; 距离调整 range adjustment; 距离误差 range error; 距离显示器 range display; 距离修正 [军事] range adjustment; 距离坐标 range coordinate
• 几何,几何学    geometry
• 超几何    hypergeometric
• 复几何    complex geometry
• 环几何    ring geometry
• 几何板    geoborad
• 几何比    geometric proportion
• 几何场    geometric field
• 几何尺    rectangular protractor
• 几何的    geometric =geometrical; geometrical
• 几何和    geometric sum
• 几何化    geometrization
• 几何级    get mildew
• 几何解    geometric solution
• 几何课    br /44．geometry class; geometry class
• 几何狂    geometric genus
• 几何类    geometric class
• 几何数    geometry number; ng

• The distance metric is good to measure geometric errors between original mesh and the result , but it is not good for preserving the shape of the original model
  几何距离能很好地控制简化后的网格与原始网格之间的误差，但在保持形状特徵上相对较弱。
• First classifier chooses two classes whose matching distance between it and paper currency is bigger than others from all class . then in the second stage , we extract some new feature and improve the classifier to generate the last result . in the stage of defect detection for paper currency , we advances a homogeneity based algorithm for the detection of scratch and cracks appearing on paper currency , in which the homogeneity feature of the sensed paper currency image is first constructed to locate the pixels that probably been polluted , the image registration algorithm is subsequently used to overlay the sensed and reference paper currency image
  在特征提取中,我们对基于方向块的特征提取方法进行了分析,在此基础上针对美元特点,对图像方向块的划分方式做了研究,并提出了基于几何距离的特征提取方法;在分类器设计中,我们采用了lvq网络对纸币进行学习与分类,并提出了一种具有两层结构的分类算法,第一层首先对输入的特征向量进行粗分类,选定与特征向量匹配距离最大的两类币种,进入第二层分类器;在第二层分类器中,我们通过研究进入该模块两类币种特征块的相关性,重新设计了特征向量,同时对分类器进行改进,最终实现对纸币的分类。
• This paper introduces the development of data mining and the concepts and techniques about clustering will be discussed , and also mainly discusses the algorithm of cluster based on grid - density , then the algorithm will be applied to the system of insurance ? among the various algorithms of cluster put forward , they are usually based on the concepts of distance cluster o whether it is in the sense of traditional eculid distance such as " k - means " or others o these algorithms are usually inefficient when dealing with large data sets and data sets of high dimension and different kinds of attribute o further more , the number of clusters they can find usually depends on users " input 0 but this task is often a very tough one for the user0 at the same time , different inputs will have great effect on the veracity of the cluster ' s result 0 in this paper the algorithm of cluster based on grid - density will be discussed o it gives up the concepts of distance < , it can automatically find out all clusters in that subspaceo at the same time , it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases o
  在以往提出的聚类算法中，一般都是基于“距离( distance ) ”聚类的概念。无论是传统的欧氏几何距离( k - means )算法，还是其它意义上的距离算法，这类算法的缺点在于处理大数据集、高维数据集和不同类型属性时往往不能奏效，而且，发现的聚类个数常常依赖于用户指定的参数，但是，这往往对用户来说是很难的，同时，不同参数往往会影响聚类结果的准确性。在本文里要讨论的基于网格密度的聚类算法，它抛弃了距离的概念，它的优点在于能够自动发现存在聚类的最高维子空间；同时具有很好的处理高维数据和大数据集的数据表格的能力。

• 几何距离的日语：きかがくてききょり