1) half spaces
高维半空间
1.
We have obtained the analogue of Luecking s result on half spaces R+n+1 for the case 2≤p≤q≤.
在单位圆盘上广义Carleson测度与H~p函数导数的关系可以推广到高维半空间上,但只解决了2≤p≤q<∞的情形。
2) high dimensional space
高维空间
1.
Using the properties of SO(n) group and the symmetry of crystal, we study the possible symmetry operation of the super lattice in high dimensional space.
利用SO(n)群的性质和晶体的对称性,研究了高维空间中超方格点阵的可能的对称操作,得到了在4、5、6维空间不仅存在5重轴,而且还存在8、10、12重轴等。
2.
Analyzing the geometry meaning of different structure neurons in high dimensional space, high dimensional geometrical distribution of the sample set in the feature sp.
利用这一理论,从不同结构神经元模型在高维空间中的几何意义出发,通过对一种新型的神经网络的构造,实现了对不同类样本在高维空间中形成的不同形状几何体的覆盖,从而达到分类的目的。
3) higher dimensional space
高维空间
1.
Geometric inequalities and its applications in higher dimensional space;
高维空间几何不等式及其应用
2.
A generalization of finite increment theorem in higher dimensional space
有限增量定理在高维空间上的推广
3.
Topological properties of higher dimensional spaces are analyzed and a standard graphic method to express such spaces is deduced.
分析高维空间的拓扑特性,并给出高维空间的标准作图法则。
4) high-dimensional space
高维空间
1.
A dynamic KPPCA was developed for process monitoring to eliminate these disadvantages by mapping the compressed data matrix extended by the time series into a high-dimensional space by a kernel function.
为克服上述缺点,提出一种基于动态KPPCA的过程监测方法,利用核函数将经过压缩的动态增广数据映射到高维空间,然后利用PPCA对满足线性关系的过程变量映射值进行监测。
2.
In the elements of statistical learning,the nearest-neighbor metbod is a very intuitive and imporant learning method,but the approch and our intuition breaks dowm in high-dimensional space for the″dimension disaster″problem.
在统计学习理论中,最近邻法是一种非常直观,重要的学习方法,但是在高维空间中由于"维数灾难"问题,该方法将失效。
5) high dimension space
高维空间
1.
A new method to decrease the scale of training data is proposed,which is to find the border of the two categories in the mapped high dimension space.
提出一种新的减小样本集规模的方法;在映射后的高维空间中寻找两种类别的交界部分,交界部分上的样本作为学习样本。
2.
In this paper a collection of high dimension convex envelope is regarded as the satisfied coverage of the complicated geometrical body formed by characteristics of the same type things in high dimension space,and an effective algorithm of determining whether or not one point is within collection of high dimension convex envelope is given.
把高维凸包络的并视为同类事物特征在高维空间中形成的复杂几何形体的满意覆盖,给出了判定一点是否属于高维凸包络的并的有效算法,变通地解决了一个在传统的解析几何框架内直接计算的不适定难题。
6) two-dimensional semi-space
二维半空间
补充资料:半空
1.半数已空。 2.谓空中。
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