欧氏距离

因此，大多数基于位置的查询，如k近邻查询，利用物体之间的欧氏距离。

Consequently , distance-based queries , like the k Nearest Neighbour query , use the Euclidean distance between object .

这种方法可以提高识别的精度和速度。对图像的识别分别使用了基于神经网络的BP算法和按欧氏距离度量的图像识别算法。

BP algorithm based on neural network and " euclidean distance " criterion are adopted to recognise the images .

基于欧氏距离的矩形Packing问题的确定性启发式求解算法

A Deterministic Heuristic Algorithm Based on Euclidian Distance for Solving the Rectangles Packing Problem

并对web流数据的相异度分类度量，定量属性使用欧氏距离和曼哈坦距离度量；

And it is for web classify measure degrees differents for flowing data , last Europe attributes quantitative from and Kazakhstans graceful the smooth from tolerance ;

介绍和分析了K-means聚类算法，并对Web文档聚类中的欧氏距离进行改进。

The clustering algorithms of K-means were introduced and analyzed . Then we improved the Euclidean Distance on web documents clustering .

用SPSS11.0FORWINDOWS软件，以欧氏距离平方为系数，采用组间连接法，对45份代表资源进行聚类分析。

The cluster analysis was done by using squared Euclidean distance coefficient and linkage between groups cluster method in SPSS 11.0 for Windows software .

多hCPFSK信号的最小平方欧氏距离

The minimum squared Euclidean distance of multi - H CPFSK signal

提出定点ICA算法结合均一化欧氏距离的人脸识别方法。

A new Face Recognition method base on Fixed-Point algorithm and mean - Euclidean distance is presented .

kmeans聚类要求每个像素要和所有聚类中心求欧氏距离，当聚类数很多时，这是一个相当耗时的工作。

Every pixel in the super space is required by K-means algorithm to calculate Euclidean distance for clustering . When there are many class centers , this is a rather time consuming work .

根据交通源之间的拓扑关系和相关系数及欧氏距离，提出了GIS条件下的交通区优化划分方法；

Then provide the way to divide traffic zone optimally based on GIS , according to the topology relationship among traffic origination and to the Correlation coefficient and Euclidean distance ;

本文算法是有别于传统近似欧氏距离的并行计算方法，可应用于传统IC硬件或数字信号处理芯片（DSP）。

In this paper , we propose a parallel algorithm deferent from the traditional Quasi-Euclidean distance transformation . It can be used in traditional IC hardware and DSP .

传统的K-MEANS算法以数据点之间的欧氏距离为测度，误差平方和为目标函数。

The traditional K-MEANS take Euclidean distances of each observation as the measurement , and error square sum the objective function .

在含5%测量误差的情况下，ART重建结果与原始浓度分布的欧氏距离达0.1680，而多目标优化重建算法下的该值仅为0.0325。

Under the case with 5 % measuring error , Euclidean distance between ART reconstruction result and the original concentration distribution is up to 0.1680 while the value is only 0.0325 under the case using multi-criteria optimization reconstruction algorithm .

分析了全响应连续相位调制（CPM）信号和部分响应CPM信号简化状态格状图的最小平方欧氏距离。

The minimum square Euclidean distance of the reduced state trellis is analyzed for full response CPM and partial response CPM .

在分类器的设计上，我们用留一法将9套手势图像划分为8套设计集和1套测试集，再用DP匹配算法计算测试集中手势与设计集中手势的欧氏距离。

In the classifier designing , system splits the 9 sets of gesture images into one testing set and 8 designing sets firstly .

在分类器设计方面，最初采用了简单的加权欧氏距离判别法，然后利用了BP（Back-Propagation）网络，之后提出了一个数据融合的混合实现方案。

As to classifier designation , first simple Euclidian distance classifier is used , then back-propagation network is utilized . At last a data fusion scheme is realized .

常用的核函数RBF只具有局部性，根据像素间欧氏距离作为两个类别的亮度差异性测量。

Common kernel function RBF only has a local characteristic , with the measurement of the difference in brightness according to the Euclidean distance between pixels of two types .

首先用PCA提取人脸主分量，计算测试样本与各类的欧氏距离，并通过构造的转换函数获得子决策；

Firstly , extracting face principal components by PCA , and computing the Euclid distances between testing sample and classes , then getting sub-decisions by a transform function .

将该方法应用于往复式压缩机气阀的故障诊断中，比较J散度与欧氏距离和相关系数在分类中的效果，证实了基于J散度的模式分类方法的分类结果更加准确。

Then the method was applied to the fault diagnosis of the reciprocation compressor valve . Compared with other measures , J divergence is proved to be more effective .

提出了一种点云和CAD模型之间的目标相似度测量方法，将归一化欧氏距离作为相似性的测度，据此实现目标的分类识别。

To measure the similarity between target point cloud and CAD models , a measurement algorithm is proposed which using the normalized euclidean distance as similarity measure . The target recognition is based on this similarity measure .

使用多通道二维Gabor滤波器来提取这些纹理的特征，并使用加权欧氏距离分类器来完成匹配工作。

We employ the well-established 2-D Gabor filtering technique to extract features of such textures and a weighted Euclidean distance classifier to fulfil the identification task .

采用欧氏距离只能发现球状的簇，改用Voronoi距离就可以形成任意形状的簇，更加符合实际效果。

It can globular clusters by using Euclidean distance , but can form honeycomb-shaped clusters using Voronoi distance , being more realistic results .

结果形态多样性指数和钉螺种群内个体间的欧氏距离均与经度明显相关，相关系数分别为0.719（P<0.01）和0.662（P<0.01），而与纬度均无明显相关性（P均>0.05）；

Results Longitude correlated significantly with Shannon-Winer index and Euclidean distances within populations , and the correlation coefficients were 0.719 ( P < 0.01 ) and 0.662 ( P < 0.01 ) respectively . However , latitude did not correlate significantly with them .

动态时间弯曲（DTW）距离度量对于时间序列具有比欧氏距离很强的优势。

Dynamic Time Warping ( DTW ) distance has great advantage over the Euclidean distance in measuring the time series .

在得到的过滤后的图像特征中，应用K均值算法对特征进行聚类索引，然后利用欧氏距离来限制过度密集的特征点，从而得到描述图像特征的特征向量。

In the filtered image feature , application of K means algorithm to cluster features index , then using the Euclidean distance to limit excessive intensive feature points , which are descriptive of the image feature vector .

传统TOPSIS方法采用固定的欧氏距离作为距离度量，各属性间的补偿关系固定。

Euclid distance metric was adopted in the Traditional TOPSIS , it leads to the problem that the compensatory relationships between each properties are fixed .

欧氏距离是精确的L2范数距离，但是由于欧氏距离的非线性，不利于各种并行算法和加速算法的设计与实现，因此在应用中各种变形的加权距离作为欧氏距离的近似得到了实际推广。

Because of the nonlinearity of euclidean distance , it is not convenient to design parallel and fast algorithm for it . Many deformed weighed distance transformation is adopted in practice .

我们称赋予了通常欧氏距离和上述增长条件的非负Radon测度μ的欧氏空间为非齐型空间。

So we call the Euclidean space Rd , which is endowed with the usual Euclidean distance and a non-negative Radon measure μ only satisfying the above growth condition , a non-homogeneous space .

k-means聚类通常采用欧氏距离作为距离度量方法，但由于高维空间数据存在噪声且具有稀疏性，使得聚类效果显著降低，影响图像的表达。

The performance of k-means clustering severely degraded when Euclidean distance was used as the similarity measurement method because of the existence of the sparsity and noise in high-dimensional data .

为克服缺点（1），我们使用基于核函数的距离测度取代FCM中的欧氏距离，并使用加权模糊聚类的方式保证了计算的简洁性。

To overcome the first problem of FCM and keep the computation simplicity , we replace Euclidean norm with kernel-induced distance and get the fuzzy partition result with weighted fuzzy clustering .