k means的例句大全

Experimental results show that this new classifier can realize high quality unsupervised image classification, which outperforms the traditional K means classifier.

实验结果表明，该种分类器能很好地实现对纹理粗糙程度模式的无监督分类，其分类性能要明显好于传统的K均值分类器。

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The method models the region with the joint histogram of local binary pattern and the labels of K means algorithm, and G-statistic is used to measure the difference of two histograms.

算法采用区域的局部二值模式特征和K均值聚类算法获得的影像标记构建联合直方图，对区域进行建模，采用G统计量度量直方图相似性。

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It also resolves the initial parameter problem of K means algorithm.

而且解决了K-means聚类的参数选择问题。

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Results Fuzzy K means clustering algorithm can segment white matter, gray matter and CSF better from the MR head images.

结果模糊K－均值聚类算法能很好地分割出磁共振颅脑图像中的灰质、白质和脑脊液。

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Using K Means which can automatically cluster trajectories, a new algorithm based on trajectory space similarity distance is presented, and it is applied to classify trajectory.

应用K均值自动聚类算法，提出了一种新的基于轨迹空间相似距离的轨迹分类算法，对以上获得的有效轨迹进行分类。

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In this paper, an improved K means clustering algorithm is presented to accelerate clustering process with more and more classes becoming stable by judging with neighbor centers nearest to the pixel.

改进后的K-means聚类算法使类内像素只通过和相邻的聚类中心进行距离计算来聚类，由于随着算法的迭代进行，大量类的状态基本固定，因此使得聚类速度不断加快。

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Genetic k Means Algorithm for Clustering of Large Scale Vector Space

大矢量空间聚类的遗传k-均值算法

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Analysis on K means Clustering Algorithm and Its Application in Teaching Quality of Teacher

K-means聚类算法分析及在教师授课质量评价中的应用

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Fuzzy K means Clustering Algorithm and It's Application Study in Segmentation of MR Head Images

模糊K-均值聚类算法及其在磁共振颅脑图像分割中的应用研究

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A data matrix based on the signal gain of cellular networks and some criterion functions are designed for K means clustering.

在聚类分解中，以测试点信号增益矩阵构造聚类分解数据，并给出了收敛判定函数和相似度计算方法。

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The results illustrate that the best centroids initialization in K means clustering is to select vectors characterized the structure of the dataset.

结果表明：若对基因表达数据进行K-均值聚类分析，最好采用能反映数据结构特征的向量对质心进行初始化。

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In this paper, we use direct classification and K means algorithm to distinguish the high cloud, meddle cloud, low cloud and earth ′ s surface.

应用模式识别中区域聚类法即最近邻简单试探法和K-均值聚类算法来完成高云、中云、低云和地表的区分。

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Fuzzy K-Prototypes ( FKP) algorithm integrating K-Means and K-Modes algorithm is suited for clustering mixed numeric and categorical valued data.

模糊K Prototypes（FKP）算法融合了k means和k modes对数值型和符号型数据的处理方法，适合于混合类型数据的聚类分析。

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In this paper a modified K-means algorithm ( FKM) is presented.

本文提出了一种改进的k means算法（FKM）。

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In credit card analysis, use K means algorithm, one of the method in clustering algorithm, to find the minimal partition of DSS.

在信用卡信用评分系统中，运用基于划分的聚类方法k means算法，试图找出使DSS函数值最小的划分，该算法的准则函数与信用评分评级的均方差准则相吻合。

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Cluster analysis based on K means and immune algorithm

基于K均值和免疫算法的聚类分析

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In this paper, a credit rating model based on soft computing is proposed which is a integration of fuzzy mathematics and genetic algorithm and use Fast Genetic k means Algorithm to cluster.

提出了一种基于软计算的企业资信评估模型，它集成模糊数学和遗传算法，用快速遗传k均值算法进行聚类。

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Also, because removing the noise involved in this article is differ from traditional de-noising, coupled with the requirements in real-time system itself of study for the practical problems, we proposed a removing noise method based on the k means clustering.

由于本文中涉及的去除噪声的实际问题和传统的意义上的去噪有所区别，结合系统本身实时性的要求，提出了基于k均值聚类的去除底噪声方法。

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SCLA are soft competition schemes of the learning algorithms, in which the center vectors are trained based on the k means algorithm, and can remedy the problems of the dead node and the sensitivity to initial weight vectors that the latter algorithms have.

SCLA是RBF网络基于k均值方法训练中心向量的学习算法的软竞争格式，它可以克服后者对初始值敏感和死节点的问题。

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K means algorithm applied in vector quantization strongly depends on the selection of the initial codebook, and if not given a good initial codebook it can easily be trapped in local minima.

由于传统的K-均值算法在用于矢量量化时强烈依赖初始码书的选取，如果初始码书选取不好，则很容易陷入局部最小点；

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