1) kernel K-means clustering
核K-均值聚类
1.
Aimming at the shortage of Sparse Kernel Principal Component Analysis(SKPCA) in feature extraction,a novel feature extraction method based on the kernel K-means clustering and the SKPCA for speaker recognition is proposed.
针对稀疏核主成分分析方法在特征提取中的不足,提出了一种基于核K-均值聚类的稀疏核主成分分析(Sparse KPCA)的特征提取方法用于说话人识别。
2.
We proposed a novel approach that is based on the kernel K-means clustering and support vector machine(SVM),set up a support vector machine for each two speakers,here kernel K-means clustering is exploited to input speech signal of SVM into a given amount of clusters choosing the effective samples as the input of SVM.
提出了基于核K-均值聚类方法与支持向量机结合的说话人识别方法,为每两个人建立一个支持向量机,对支持向量机输入的语音信号先进行核K-均值聚类,并选取有效样本作为支持向量机的输入,本文提出的聚类方法能够去更好的聚类并约简数据,提高了识别率。
2) k-mean clustering
k均值聚类
1.
The combined algorithm that changes thresholding based on histogram into thresholding based on H in HSI color space and selects the pixels according to its R,G,B value,before thresholding;Three kinds of algorithms in image segmentation were analyzed by testing,including K-mean clustering based on RGB color space proposed by Liju Dong,etc,thresholding based on histogra.
提出一种混合算法,将一种基于灰度直方图的阈值化分割算法应用到了HSI颜色空间上,利用H进行阈值化,在阈值化之前,先根据R、G、B值对像素进行了筛选;文章应用董立菊等人提出的-种基于RGB空间的K均值聚类算法、一种基于灰度直方图的阈值化算法和混合算法,以目标颜色为特征,对彩色图像进行了分割;针对特定的视频跟踪系统,对各结果进行了比较,得出了结论,找出了较优算法——混合算法效果较理想,能够较有效的分割目标,为后续跟踪工作做好了前期处理工作。
2.
At last blocks are classified by K-mean clustering method.
首先对图像做小波变换和重构,并抽取字幕区域特征,再分块计算统计特征;然后对子块进行K均值聚类,实现字幕区域分割。
3.
In this paper,we first present a fast K-mean clustering algorithm by using Partial Distortion Search(PDS) to complete the nearest neighbor searching in traditional K-mean clustering algorithm.
利用部分失真搜索求解传统K均值聚类算法中的最近邻搜索问题,显著地减少了传统算法的乘法次数,从而提高了聚类速度;然后用改进后的聚类算法来加速分形编码:首先将定义域块聚类并为每个类建立一棵KD-Tree,编码时对每个值域块先后用部分失真搜索与近似最近邻搜索得到与其距离最近的若干KD-Tree及其上的若干最近邻,而其最优匹配块即由后者产生。
3) K-means cluster
K均值聚类
1.
When being segmented by K-means clustering algorithm,points cloud dataset with conglomeration feature presents a better segmentation.
论文指出,对于分布呈现类内团聚状三维点云模型,K均值聚类分割可以得到较好的结果。
2.
First,we compute the LVPS dictionary by K-means clustering.
首先,利用K均值聚类算法获得LVPS dictionary;然后,利用获得的LVPS对人脸进行建模,该方法比传统的建模方法计算更简单;最后,利用分块后的LVPS加权直方图索引进行人脸识别。
3.
A multiple kernel SVM based on K-means cluster algorithm was proposed.
考虑到乳腺微钙化簇样本分布不平衡以及特征的多样性,提出了基于K均值聚类的多核支持向量机。
4) k-means clustering
K-均值聚类
1.
Chinese text chunking based on improved K-means clustering;
基于改进K-均值聚类的汉语语块识别
2.
Semi-supervised improved K-means clustering algorithm
半监督的改进K-均值聚类算法
3.
Methodology study on instance retrieval of conceptual design based on roughness set and K-means clustering
基于粗糙集和K-均值聚类的概念设计实例检索方法研究
5) k-mean clustering
k-均值聚类
1.
A fast fractal image compression algorithm based on K-mean clustering optimization;
一种基于K-均值聚类优化的快速分形图像压缩算法
2.
Reasearch on multirada data fusion algorithm based on K-mean clustering;
基于K-均值聚类的多雷达数据融合算法研究
3.
Image segmentation was based on k-mean clustering.
方法:利用薄层CT获取原始数据,运用k-均值聚类算法进行图像分割,结合VT(KVisualization Toolkit)技术进行可视化模型的建立。
6) K-means cluster
K-均值聚类
1.
Then the features of wavelet textures in the image are evaluated,and k-means cluster algorithm used to classify the image into text area,simple background area and complex background area.
该算法首先对图像进行二维小波变换,设置滑动窗扫描高频子带,计算滑动窗内图像的小波纹理特征,采用k-均值聚类算法将图像分为文本区域、简单背景区域和复杂背景区域,最后对文本区域进行形态运算,精确地定位文本区域。
2.
A recognition method based on HMM and K-means cluster is proposed through extracting LPC characteristic from acoustic target.
提出一种隐马尔可夫模型和K-均值聚类混合模型的声目标识别方法。
3.
The hidden unit centers are computed by K-means cluster algorithm as the clustering number has been selected by AGA.
基于目前RBF网络学习方法中的一些不足,提出了一种基于AGA的混合学习方法,即应用AGA对网络隐单元RBF个数和宽度σ同时优选,并将最佳隐单元数作为K-均值聚类数得到隐单元中心,隐层到输出层的权值由LS法确定。
补充资料：1,3-丁二烯低聚的均聚物
CAS：68441-52-1
中文名称：1,3-丁二烯低聚的均聚物
英文名称：1,3-Butadiene, homopolymer, oligomeric
中文名称：1,3-丁二烯低聚的均聚物
英文名称：1,3-Butadiene, homopolymer, oligomeric
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
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