- 更多网络例句与生成子空间相关的网络例句 [注：此内容来源于网络，仅供参考]
-
These two things are what we will study in sectionⅢ. We call them GS and PS. In this section we will study the impact of the two and the relationship between them. Of course, there are some other ways to observe the problem. One of them is to consider the period and the distribution of eigenspace. These are what we will study in sectionⅡ.
这两方面也就是本文中第三章所讲的生成序列和扰动序列，这一章中我们主要研究了这两个因素对分数傅立叶变换算子的影响及它们之间的关系；当然还有另一种看问题的角度就是从算子的周期性和特征子空间的分布的角度，这就是第二章中所介绍的内容，在这里我们给出了两个三周期的分数傅立叶变换算子并比较了其特征性质。
-
Each search direction is generated in the subspace spanned by two conjugate directions, and can be guaranteed descent.
每个搜索方向在由2个共轭向量张成的子空间中生成，并保证了搜索方向的下降性。
-
Based on LML theory frame and its algebraic model, geometric model and learning axiom systems, going for further study, this paper presented orbits generated algorithm of learning subspace in LML and applied it to corresponding examples, such as classify of human, chemical composition of wine and eight data sets including Soybean-Large, etc.
本文在李群机器学习的理论框架上，以李群机器学习的代数模型、几何模型、学习的公理系统为基础作进一步研究，给出了李群机器学习的学习子空间轨道生成算法，将该算法应用于人群分类，葡萄酒化学成分分类以及大豆等八个专用数据集的分类，取得了满意的结果。
-
Therefore, the main characteristics of this paper are:(1) Given orbits generated correlation theory of learning subspace which is the base of researching algorithm;(2) Advanced orbits generated Breadth first, Depth first and Heuristic algorithms, enrich and develop the basic content of LML further;(3) Given corresponding examples to validate the algorithms. However, all the work is tentive and much needs advanced research.
由此可以看出，本文的特色主要体现在以下几个方面：(1)给出了李群机器学习子空间轨道生成的相关理论，为研究学习子空间轨道生成算法奠定了基础；(2)提出了李群机器学习子空间广度优先轨道生成学习算法，深度优先轨道生成学习算法以及带有启发信息的轨道生成学习算法，丰富和发展了LML的基本内容；(3)对所提出的算法给出了相应的实例验证。
-
By using the technigue of pointwise representation, this paper goes somewhat deeper into the theory of fuzzy subspaces. It has mainly studied the structure of generated fuzzy subspaces, and various relations between a fuzzy set and its generated fuzzy subspace are given. Furthermore, a pointwise representation theorem for fuzzy subspaces is presented.
本文利用点态表示方法进一步探讨了Fuzzy子空间理论，着重研究了生成Fuzzy子空间的结构，给出了Fuzzy集与其生成Fuzzy子空间之间的各种关系，进而，文章还给出了Fuzzy子空间的一个点态表示定理。
-
In chapter 2, first of all,we characterize subspace V_0 of multiresultion analysis {V_j}_~ based on invariant subspace. Subsequently, we consider the equivalent conditions among orthogonal multiwavelet Ψ=(Ψ_1,Ψ_2,...Ψ_r)~T, subspace, basis and dimension of subspace, properties of filter function matrix P are dealt with.
第二章讨论了多尺度分析{V_j}_~中子空间V_0的性质，进而讨论了多尺度分析生成的多重正交小波Ψ=(ψ_1，ψ_2，…，ψ_r)~T和子空间V_0以及基和维数之间的等价关系，分析了滤子函数矩阵P的性质，最后给出了r阶矩阵函数P生成尺度函数Φ的充分条件。
-
It relies on the facts that the rows composed of all the image points span the same linear subspace as the rows composed of the 3D space points and that the basis of the subspace can consist of the two rows composed of the first image points and a row vector which is orthogonal to the former.
所有图像序列构成的行向量与3维空间点构成的行向量所生成的子空间是同一线性子空间，而且由第1幅图像点构成的2个行向量外加1个行向量就可以组成该子空间的一个基底。
-
An iterative factorization method based on linear subspace for projective reconstruction is presented in the paper. It relies on the facts that the rows in the matrix including all the image points span the same linear subspace as the rows in the matrix including space points and the fact that any basis of the subspace can be regarded as projective reconstruction. The projective reconstruction and the depth factors are obtained based on linear iteration.
摘要该文提出了一种基于子空间线性迭代的射影重建方法，该方法利用所有的图像序列构成的行向量生成的线性子空间之和与射影重建结构点构成的行向量生成的子空间是同一线性子空间及在该子空间中任何一个基底都可以作为射影重建的特性，线性迭代地求取射影重建及图像深度因子。
-
The method relies on the facts that the row vectors composed of all the image points span, the same linear subspace as the row vectors composed of 3D space points span and that a basis of the subspace can consist of two row vectors composed of all the image points and one row vector in 3D space that is orthogonal to the former. The row vector can be determined, and the 3D reconstruction is accomplished. The novelty lies in the fact that the method can treat all the image points uniformly.
利用所有图像序列构成的行向量生成的子空间之和与三维空间点构成的行向量生成的子空间是同一线性子空间、同时由所有图像点构成的2个行向量外加一个行向量就可以组成该子空间的一个基底的特性，线性地求取子空间中的行向量，最后完成三维重建。
-
In section 1.3, taking the advantage of the classification by the index arrays ,pointed out the space spanned by class A,B,C respect!vivety are subalgebras of NQ. We also show that the subspace spanned by the elements of length 2 in C is an abelian subalgebras of NQ,denoted by H.we find H is contained in the center of subalgebra P of the subspace spanned by the elements in class C.
在1.3节中，应用指标数组我们讨论了各类元素的性质，指出其第A，B，C类元各自张成的子空间均是N_Q的子代数，特别是C类中所有长度为2的元素张成了N_Q的Abel子代数H，且含于C生成的子代数P的中心Z中。
- 更多网络解释与生成子空间相关的网络解释 [注：此内容来源于网络，仅供参考]
-
generated group：生成群
生成 generate | 生成群 generated group | 生成子空间 generated subspace
-
generated subspace：生成子空间
生成群 generated group | 生成子空间 generated subspace | 母圆 generating circle
-
generating circle：母圆
生成子空间 generated subspace | 母圆 generating circle | 母锥 generating cone
-
generating series：生成级数
生成程序 generating routine | 生成级数 generating series | 生成子空间 generating subspace
-
generating subspace：生成子空间
生成级数 generating series | 生成子空间 generating subspace | 生成 generation