1) separation matrix
分离矩阵
1.
The permutation alignment methods using DOA estimation is discussed and a new scheme based on separation matrix initialization is proposed.
讨论了频率对准算法中基于DOA估计的方法,并提出了一种基于分离矩阵初始化的频率对准方法,此方法易于实现。
2) Semiseparable matrix
半分离矩阵
3) semidiscrete decomposition
半离散矩阵分解
1.
Research of the improved semidiscrete decomposition on web page information retrieval;
半离散矩阵分解改进算法在网页信息检索中的应用研究
4) Signal separation matrix operator
矩阵分离算子
5) distance matrix
距离矩阵
1.
Study on the improved topological index of distance matrix and its application;
改进的距离矩阵指数W_1~*及其应用研究
2.
The element of matrix in the distance matrix S*_~ij is defined and the topological index W* based on the distance matrix is defined as well.
定义了矩阵元Sij和基于距离矩阵的拓扑指数W。
3.
According to the adjacency matrix and distance matrix of the two units, the sequential formulas to calculate Wiener topological indices or molecular topological indices have been given.
将任一枝状烷烃拆分为由直链单元与余下烷基两部分构成,从隐氢图的距离矩阵和邻接矩阵出发,分别导出了计算该烷烃的MTI和Wiener拓扑指数的递推公式,进而证明了直链烷烃的MTI与Wiener指数间存在3。
6) mean dispersion error matrix
离差矩阵
1.
Considering the generalized linear regression model y=Xβ+e,e～N(0,σ 2W) and its prediction problem of biased estimation, this paper discusses its superiority of the optimal and classical predictors based on the ridge estimation by criteria of mean dispersion error matrix and generalized risk function.
针对有偏估计的预测问题 ,以岭估计为基础 ,以离差矩阵MDE(MeanDispersionError)和广义风险函数为判别准则 ,对广义线性回归模型 {y =Xβ +e ,e～N(0 ,σ2 W ) }的最优预测量与经典预测量的最优性判问题进行了讨论。
2.
Considering the generalized linear regression model and its prediction problem of biased estimation,this paper discusses its superiority of the optimal and classical predictors based on the shrunken principal estimator by criteria of mean dispersion error matrix and generalized risk function.
本文以压缩主成分估计为基础,对广义线性模型的最优预测与经典预测的最优性判别问题进行了讨论,获得了在离差矩阵判别准则和广义风险函数判别准则下判断两类预测量最优性的一个充分条件,为进一步研究基于有偏估计关于两类预测量的最优性判别问题提供了一种方法和思路。
补充资料：分离
分离
①两者解离，如脑脊液检查出蛋白细胞分离现象，见于格林巴利综合征。②隔离的意思，如对传染病患者、恢复期患者和接触者，为防止传染源扩散将其与正常人分离隔绝，避免接触传染。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条