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本文针对SLAM算法中数据关联过程，提出了一种简单易行的改进方法，将欧氏距离与马氏距离结合用于数据关联。算法不必计算地图所有特征与所有量测之间的马氏距离，而是首先利用相对简单的欧氏距离计算缩小了待关联特征的搜寻范围。利用人工合成数据的仿真结果表明，改进后的数据关联方法可以大幅减少系统计算量，提高关联效率，且不会造成错误关联的增加。-This article SLAM algorithm for data association process, a simple method to Euclid
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本文针对SLAM数据关联中使用最为广泛的最近邻方法作了改进，利用特征估计位置与载体预测位置之间的欧氏距离计算代替了全部特征与每个量测之间的马氏距离计算，避免了大量的矩阵乘法计算。该算法简单易行，降低了算法的计算复杂度，有利于SLAM算法的实时执行，且关联效果与全局最近邻法相同-In this paper, SLAM data association in the most widely used methods of improving the nearest neighbor, using t
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Euclidean节点定位是由美国路特葛斯大学利用距离矢量路由和GPS定位原理提供出一种分布式自主定位算法-Node localization Euclidean path by the Rutgers Universitys use of distance vector routing and GPS positioning to provide a principle of self-positioning algorithm for distributed
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以matlab撰写的ACO算法，很适合用于研究Ant colony algorithms。-Write a MATLAB program to solve the following given city Euclidean traveling salesman problem (given lattice points) by using ant-cycle algorithm.
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Euclidean Distance Transform has been widely studied in computational geometry, image processing, computer graphics and pattern recognition. Euclidean distance has been computed through different algorithms like parallel, linear time algorithms etc.
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In this paper, we present two novel class-based
weighting methods for the Euclidean nearest neighbor algorithm
and compare them with global weighting methods
considering empirical results on a widely accepted time series
classification benchm
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kMeansCluster - Simple k means clustering algorithm
Author: Kardi Teknomo, Ph.D.
Purpose: classify the objects in data matrix based on the attributes
Criteria: minimize Euclidean distance between centroids and object points
For mo
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Implementation of the extended Euclidean algorithm for polynomials over GF(2^m
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实现了向量的聚类算法，输入三个参数：向量集合、划分数目和设定的阈值，就可得到聚类结果，以欧式距离作为距离判别准则。-Realize the vector of the clustering algorithm, the input three parameters: vector set and the division of number and set threshold, can get clustering results to the Euclidean distance as a c
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一种实现流形学习ISOMAP的matlab算法 即将mds中的欧氏距离替换为测地距离 保证其拓扑空间不变 实现数据的特征提取-An matlab program that can realize ISOMAP algorithm. To replace the Euclidean distance to the distance measurement.To ensure the realization of the same topological space data feature ext
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proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details
in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean
distance between the two correspon
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按照模式相似性测度算法计算待测样品与样品库中的样品相似度，包括模块匹配法、基于PCA的模块匹配法、基于类中心的欧氏距离法和马氏距离法-Similarity measure in accordance with the mode algorithm to calculate the sample similarity in the sample and sample libraries , including the module matching method based on PCA modu
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基于欧几里得距离的聚类分析算法程序，只需要用户输入矩阵即可完成-Euclidean distance-based clustering analysis algorithm procedure, but requires the user to complete input matrix
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用于建模数据分集。计算各样本点的欧式距离，首先将距离最大的两个点放入第一个样本集，接着将余下距离最大的两个点放入第二个样本集，依次计算，将剩余数据点分别放入上述两个样本集。- This algorithm starts by selecting the two points furthest from each other (euclidean distance) and puts them both in a first set. Then the next two points furthe
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这6个程序可用于0—9的数字识别，算法包括：基于类中心的欧式距离法、马氏距离法、夹角余弦距离法
、二值化的夹角余弦距离法、二值化的Tanimoto测度法和增量校正算法，均为基于matlab软件编写的程序。学会了这几个算法，可以在这些程序的基础上进行扩展，对三维模型识别算法的编程有一定的帮助。-6 program can be used 0-9 digit recognition, algorithms include: class center Euclidean distance, Mah
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lloyd max algorithm is used to quantize an analog signal, uses euclidean distance condition to selectively choose the quantization levels.
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一、Cantor三分集的递归算法
选取一个欧氏长度的直线段，将该线段三等分，去掉中间一段，剩下两段。将剩下的两段分别再三等分，各去掉中间一段，剩下四段。将这样的操作继续下去，直到无穷，则可得到一个离散的点集。点数趋于无穷多，而欧氏长度趋于零。经无限操作，达到极限时所得到的离散点集称之为Cantor集。-A recursive algorithm selection Cantor ternary set a Euclidean line segment length, the three eq
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近邻分类，属于模式识别类的。对两类数据分别产生高斯分布数据，用KNN算法看这个数属于哪个类的，并求测试数据和类中每个数据的欧氏距离-Neighbor classification, pattern recognition belongs to the class. Two types of data were generated Gaussian distribution data, to see with this number belongs KNN algorithm class and
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Computes the Greatest Common Divisor of the values in array N using the Euclidean algorithm
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K-means算法是硬聚类算法，是典型的基于原型的目标函数聚类方法的代表，它是数据点到原型的某种距离作为优化的目标函数，利用函数求极值的方法得到迭代运算的调整规则。K-means算法以欧式距离作为相似度测度，它是求对应某一初始聚类中心向量V最优分类，使得评价指标J最小。算法采用误差平方和准则函数作为聚类准则函数。(The K-means algorithm is a hard clustering algorithm, which is representative of the prototy
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