线性映射的核

词组短语

kernel of linear mapping

与线性映射的核相关的网络例句 [注：此内容来源于网络，仅供参考]

In this paper the topological properties of solution set of non-autonomous differential inclusions are discussed,and the fact that the mild solution set of Cauchy is a retract of a convex subset of a Banach space is proved.

讨论了半线性微分包含解的拓扑性质，证明了非自治情况下其适度解集是一Banach空间的凸子集的收缩核，且收缩映射连续依赖于初值。

This method uses the kernel function to map the nonlinear space into a linear high dimension space.

该方法使用核函数完成非线性空间到高维线性空间的映射，避免了高维空间中的数据处理和非线性映射函数的使用。

As for the undivided linear sample space, the kernel function is needed to map onto another high dimension linear space.

对于线性不可分的样本空间，需要寻找核函数，将线性不可分的样本集映射到另一个高维线性空间。

In this algorithm, first, the nonlinear kernel mapping is used to map the face data into an implicit feature space, and then a linear transformation which produces orthogonal basis functions is performed to preserve locality geometric structures of the face image.

该算法首先利用核的方法提取人脸图像中的非线性信息，并将其投影在一个高维非线性空间，在保证各向量正交的同时，通过局部保持投影算法做一线性映射，从而更好地提取人脸非线性局部邻域结构特征。

Optimization; parametric quadratic convex programming; set-valued map; directional derivative; linear stability; solution-set map; parametric linear programming; error bound; subdifferential map; lower locally directionally Lipschitzian; upper locally di-rectionally Lipschitzian; locally directionally Lipschitzian; convex function; quasidiferential; kernelled quasidiferential; quasi-kernel; star-kernel; star-diferential; Penot diferential; subderivative; superderivative; epiderivative; set-valued optimization; set-valued analysis; subdifferential; optimization condition;ε-dual; scalization; generalized subconvexlike-cone;ε-Lagrange multiplier

基础科学，数学，运筹学最优化；集值映射；方向导数；线性稳定；最优解集映射；参数线性规划；参数凸二次规划；误差界；次微分映射；下局部方向Lipschitzian；上局部方向Lipschitzian；局部方向Lipschitzian；凸函数；拟微分；核拟微分；拟核；星核；星微分； Penot-微分；上导数；下导数； Epi-导数；集值优化；集值分析；集值映射的次微分；最优性条件；广义锥次类凸；ε-对偶；数乘；ε-Lagrange乘子

The SVM method is based on the theory of structural risk minoration. It can map the sample space to a higher feature space by selecting an optimal Kernel function, and get a linear regression in this higher feature space.

中文摘要：支持向量机方法是基于结构风险最小化原理提出的，通过采用合适的核函数将样本空间映射到一个高维特征空间，再在高维特征空间进行线性回归。

与线性映射的核相关的网络解释 [注：此内容来源于网络，仅供参考]

kernel of integral operator：积分算子核

kernel of an integral equation 积分方程的核 | kernel of integral operator 积分算子核 | kernel of linear mapping 线性映射的核

kernel of a integral equation：积分方程核

同构映射的核 kernel of a homomorphism | 积分方程核 kernel of a integral equation | 线性映像的核 kernel of linear mapping

kernel of linear mapping：线性映射的核

kernel of integral operator 积分算子核 | kernel of linear mapping 线性映射的核 | kernel preserving functor 核保存函子

kernel of a linear mapping：线性映射的核

线性映射|linear mapping | 线性映射的核|kernel of a linear mapping | 线性映射的秩|rank of a linear mapping

kernel preserving functor：核保存函子

kernel of linear mapping 线性映射的核 | kernel preserving functor 核保存函子 | key 链

linear mapping preserving zero product：保零积的线性映射

保核值映射:Kernel-range preserving mapping | 保幂等的线性映射:linear mapping preserving idempotents | 保零积的线性映射:linear mapping preserving zero product

rank of a linear mapping：线性映射的秩

线性映射的核|kernel of a linear mapping | 线性映射的秩|rank of a linear mapping | 线性映射的转置|transpose of a linear map

rank of a linear mapping：线性映射的秩

线性映射的核||kernel of a linear mapping | 线性映射的秩||rank of a linear mapping | 线性映射的转置||transpose of a linear map

kernel of a linear mapping：线性映射的核

线性映射||linear mapping | 线性映射的核||kernel of a linear mapping | 线性映射的秩||rank of a linear mapping