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维子的英文

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"维子"怎么读用"维子"造句

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  • koreko

例句与用法

  • The method analyzes initial data at different scales carefully , extracts independent signals according to the information maximization criterion , and monitors the process in real time in a low - dimensional subspace of data
    该方法对初始数据进行多尺度细化分析,并根据信息最大化准则提取独立元信号,在数据的低维子空间上对过程进行实时监控。
  • Subspace method wants to find out a conversion with the ability of representing the data set with the effective features in minor dimension space without decreasing the inherent information contained in original data
    子空间分析方法是近年来受到广泛重视的特征提取方法,其思想就是根据一定的性能目标来寻找一种线性或非线性的空间变换,把原始数据压缩到一个低维子空间中。
  • The main contents of this thesis are shown below : 1 . a necessary and sufficient condition of the separability of a mixed state in bipartite systems is presented by decomposing a higher dimension hilbert space into many 2 2 subspaces
    主要内容包括: 1 、提出了一种通过把高维hilbert空间投影到多个低维子空间的方法,来研究纠缠态的判据,得到了纠缠态的一个充分必要判据。
  • Some properties of the optimal path of trust region methods are shown , and the relationship between dogleg path and optimal path is analysed . this can be used to derive better approximate algorithms for solving trust region subproblems
    本文描述了信赖域方法最优曲线在二维子空间内投影的几个性质,分析了几种信赖域折线法与该投影的关系,为推导更好的求解信赖域子问题的折线近似提供理论依据
  • Face recognition based on the algebraic feature face recognition based on the algebraic feature expresses the human faces by vectors of feature too . but the vectors are algebraic feature ones . it is the projection of the human faces in the lower dimension sub - space of eigenface
    基于代数特征的人脸识别方法这类识别方法也是将人脸用特征矢量表示,但采用的是代数特征矢量:即人脸图像在由“特征脸”张成的降维子空间上的投影。
  • 2 - d superresolution direction finding methods at subarray level suited for plane phased arrays are studied . for overcoming the limitations of true array manifold ( tam ) that the expense and cost of the array calibration are high and the calibration realization is complex , five kinds of simplified array manifolds are proposed , namely direct simplified array manifold ( dsam ) , simplified array manifold based on ideal subarray patterns ( sam _ isp ) , simplified array manifold based on approximate ideal subarray patterns ( sam _ aisp ) , simplified array manifold based on gaussian subarray patterns ( sam _ gsp ) , simplified array manifold based on approximate gaussian subarray patterns ( sam _ agsp ) respectively . the performances of applying these array manifolds to music , wsf and ml methods at subarray level are analyzed
    本文研究了平面相控阵二维子阵级超分辨测向,针对真实阵列流形( tam )校正成本和代价高以及实现复杂的缺陷,提出了五种简化的阵列流形分别为直接简化的阵列流形( dsam ) 、基于理想子阵方向图的简化阵列流形( sam _ isp ) 、基于近似理想子阵方向图的简化阵列流形( sam _ aisp ) 、基于高斯子阵方向图的简化阵列流形( sam _ gsp )和基于近似高斯子阵方向图的简化阵列流形( sam _ agsp ) 。
  • Detailed work include : generalizing , analyzing , mathematically calculating , im - proving and estimating by experiments the 2d - doa estimation from the classical al - gorithm of 1 - d subspace . moreover , application and simulation of the properties of the cyclostationary of signals in parameter estimation within smart antennas area are studied as well
    具体工作包括:经典一维子空间算法推广到二维中的分析,演算,改进,实验仿真;以及信号平稳特性在智能天线参数估计中的应用和实验仿真。
  • This new approach first computes the motion vectors between a tcst face image and a neutral training image using the block - matching metbod , tben projects the motion vectors to a low dimensional subspace that is pre - trained by applying principal component analysis ( pca ) to motion vectors resulting from training imagcs with expression variations . this subspace is called an eigenmotion space
    为了提高表情人脸识别的鲁棒性,该方法首先利用块匹配的方法来确定表情人脸和无表情人脸之间的运动向量,然后利用主成分分析方法( pca )从这些运动向量中产生低维子空间,称之为特征运动空间。
  • This paper introduces the development of data mining and the concepts and techniques about clustering will be discussed , and also mainly discusses the algorithm of cluster based on grid - density , then the algorithm will be applied to the system of insurance ? among the various algorithms of cluster put forward , they are usually based on the concepts of distance cluster o whether it is in the sense of traditional eculid distance such as " k - means " or others o these algorithms are usually inefficient when dealing with large data sets and data sets of high dimension and different kinds of attribute o further more , the number of clusters they can find usually depends on users " input 0 but this task is often a very tough one for the user0 at the same time , different inputs will have great effect on the veracity of the cluster ' s result 0 in this paper the algorithm of cluster based on grid - density will be discussed o it gives up the concepts of distance < , it can automatically find out all clusters in that subspaceo at the same time , it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases o
    在以往提出的聚类算法中,一般都是基于“距离( distance ) ”聚类的概念。无论是传统的欧氏几何距离( k - means )算法,还是其它意义上的距离算法,这类算法的缺点在于处理大数据集、高维数据集和不同类型属性时往往不能奏效,而且,发现的聚类个数常常依赖于用户指定的参数,但是,这往往对用户来说是很难的,同时,不同参数往往会影响聚类结果的准确性。在本文里要讨论的基于网格密度的聚类算法,它抛弃了距离的概念,它的优点在于能够自动发现存在聚类的最高维子空间;同时具有很好的处理高维数据和大数据集的数据表格的能力。
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