为了提高支持向量机(SVM)的识别性能，提出了在常用内核的基础上构造一个组合内核函数，然后用拟牛顿算法对其超参数进行优化的方法。
To improve the performance of support vector machines (SVM), a hybrid kernel is constructed from the existing common kernels, and the hyper-parameters are optimized by using a quasi-Newton method.
利用超球支持向量机，对每类样本求得一个能包围该类尽可能多样本的最小超球，使各类样本之间通过超球隔开。
Hyper-sphere support vector machine is used to get the smallest hyper-sphere that contains most samples of a class, which can divide the class samples from others.
讨论了阶化向量空间和李超代数的基本性质。
The general properties of graded vector space and Lie superalgebras are discussed.
这种算法既无需选择传统超稳定自适应算法的补偿滤波系数，又无需对信号向量的下界要求条件。
The smooth coefficients which must be chosen in conventional hyperstable algorithm are not needed, and requirement that signal vector has a lower bound is not needed either.
在传统的空间域正则化图像复原或超分辨率图像复原方法中，是用向量2-范数度量数据逼近项和正则项。
In traditional approach, people use l_2 -norm to measure data approximation item and regularization item in regularized image restoration and super-restoration in spatial domain.
提出了一个基于同心超球面分割的支持向量预抽取方法，并在此基础上给出了HD - SVM训练算法。
A method for pre-extracting support vectors based on concentric hyperspheres division is presented in this paper, and HD-SVM algorithm based on this method is presented also.
另一类变量与向量函数呈非线性关系。 对于后一类变量，用弃舍随机方法先给出位置初值，然后将问题化为线性最小二乘问题，直接解超定方程组。
The overdetermined equations can be solved directly by using the rejection method to give the initial value of the position first and then converting the problem into a linear—square one.
在此基础上，提出了超球投影嵌入支持向量鉴别分析特征降维算法，分层次人脸拒识分类算法。
Then hierachical face recognition with the ability of rejection for non-target and classification for target is proposed.
本文提出了一种基于支撑向量机的盲超分辨率图像复原算法。
In this paper we propose a blind super-resolution image restoration algorithm based on Support Vector Machines (SVM).
本文提出了一种基于支撑向量机的盲超分辨率图像复原算法。
In this paper we propose a blind super-resolution image restoration algorithm based on Support Vector Machines (SVM).
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