在软测量建模过程中，基于支持向量机的算法能较好地解决小样本、非线性、高维数、局部极小点等问题。
In model establishment of soft-sensing, the problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine algorithm.
关于算法分析的定理证明了这种混合算法对于紧致集内的权向量构成的任意连续函数能依概率1收敛于全局极小值。
It is shown that this algorithm ensures convergence to a global minimum with probability 1in a compact region of a weight vector space.
给出了两个拓扑向量空间的乘积空间上截口定理，极小极大不等式及一个推广的不动点定理。
A section theorem, a minimax inequality and a generalized fixed point theorem where the underlying space is a product space of two topological vector Spaces, are given.
本文通过使用向量似变分不等式和半预不变凸函数来证明约束向量优化的弱极小值的存在性。
In this paper, We prove the existence of a weak minimum for constrained vector optimization problem by making use of vector variational-like inequality and semi-preinvex functions.
证明了区间空间上几个参数型KKM定理并得到了几个新型的向量值极大极小定理。
Some parametric type KKM theorems are proved in interval spaces, and soine new vector valued minimax theorems are obtained.
提出了一种新的目标轮廓特征级融合方法，求解两类模式图像的收敛动态轮廓线控制点向量差的范数平方极小化。
A new feature level fusion method of target's contour was proposed, which minimizes norm's square of the difference of control point vectors of convergent dynamic contours in two modal images.
支持向量机方法较好地解决了许多学习方法面临的小样本、非线性和局部极小点等问题，具有很好的应用前景。
SVM solves practical problems such as small samples, nonlinearity, local minima, which exist in most of learning methods, and has a bright future.
使得对拟常曲率黎曼流形中紧致子流形的研究由极小子流形和伪脐子流形情形扩展到具有平行平均曲率向量的情形。
The work makes the study of compact submanifolds in quasi constant curvature Riemannian manifolds extend from the especial case to general case.
在模糊内积空间的极小化向量定理基础上，给出了向量拟模糊正交定义，并在模糊内积空间中证明了投影定理。
Based on the minimizing vector theorem of the fuzzy inner product space, the pseudo fuzzy orthogonal vector is defined and the projected theorem of the fuzzy inner product space is testified.
在模糊内积空间的极小化向量定理基础上，给出了向量拟模糊正交定义，并在模糊内积空间中证明了投影定理。
Based on the minimizing vector theorem of the fuzzy inner product space, the pseudo fuzzy orthogonal vector is defined and the projected theorem of the fuzzy inner product space is testified.
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