几何距离 meaning in Chinese
geometrical distance
Examples
- 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。