核函数是SVM的关键技术，核函数的选择将影响着支持向量机的学习能力和泛化能力。
Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.
核函数分别采用多项式和径向基函数。
The kernel functions are generated to use polynomials and radial basic functions.
对超音速核函数法的展向积分作了改进。
Some improvements are made on the spanwise integration of the supersonic kernel function method.
对遥感方程核函数的基本性质进行了讨论。
The properties of the kernel functions of the remote sensing equations have been discussed.
在子空间聚类引入核函数以提高数据聚类性能。
Kernel function is introduced to subspace clustering to improve the clustering performance.
提出了一个基于核函数的后非线性盲分离算法。
We derive a new method based on kernel to solve the post-linear blind signal separation problem.
由协方差函数的统计特征，可给出核函数的参数估计。
According to the statistic characteristic of covariance function, parameter estimation can be given for kernel function.
然后将该核函数应用于支持向量机，对图像进行分类。
Then support vector machines using the kernel are trained to perform image classification.
研究了具有参数优化的核函数法及其在聚类问题中的应用。
It studies kernel function method with parameters optimized and its application in pattern clustering.
应用LOO估算选择的核函数模型能够较好地逼近最佳值。
The kernel function model selected by LOO estimation can approach the best value satisfactorily.
就地壳垂直形变分析的实际情况而言，不宜采用高阶的核函数。
For the crustal vertical deformation analysis, higher-order kernel function is not sortable.
为此，在研究支持向量机的基础上，给出了核函数的若干重要性质。
For this reason, several important properties of kernel function are discussed on the basis of support vector machine.
证明当分布时滞的核函数取弱核形式时，平衡点是绝对渐近稳定的。
When the kernel function of the distributed delay takes the form weak kernel, the positive equilibrium is Absolutely asymptotically stable.
具有任意曲线前缘的亚音速升力面的气动敏感性导数由核函数法给出。
Aerodynamic sensitivity for subsonic lifting-surface with arbitrary curved leading edge is evaluated by kernal function method.
最后讨论了离散尺度与小波核函数的构造，核函数选择与核参数学习。
Finally, the construction of discrete scaling and wavelet kernels, the kernel selection and the kernel parameter learning are discussed.
第一章研究了一类二阶抛物型方程组的一种新数值方法-再生核函数法。
In Chapter One, a new method of approximating the solution of second-order parabolic system using reproducing kernel function is devised.
同时，基于LF M信号的模糊域特征，提出了一种新的自适应核函数。
Then based on the features of ambiguity function of a LFM signal, a new adaptive kernel is proposed.
不同分类核函数的相互比较分析表明，线性核函数最适合黄瓜病害识别。
The comparison of different kernel functions for SVM shows that liner kernel function is most suitable for recognition of cucumber disease.
研究核函数的几何度量和几何性质，并给出核函数选择(核选择)的建议。
The geometric measures and properties of the kernel functions were studied and some Suggestions about the selection of kernel functions were presented.
核函数方法中最为出名的是流行的支持向量机算法，它其实是一系列方法。
Kernel methods are best known for the popular method Support Vector Machines which is really a constellation of methods in and of itself.
首先，用具有RBF核函数的LS-SVM离线建立被控对象的非线性模型；
The nonlinear offline model of the controlled plant is built by LS-SVM with the radial basis function (RBF) kernel.
首先，为了去除测量产生的噪声和误差，引入高斯核函数为每个采样点加权；
To filter out the noise and error arising out of various physical measurement processes and limitations of the acquisition technology, a Gaussian weight is assigned to each point acquired.
核函数方法中最为有名的是流行的支持向量机算法，它实际上是一系列方法。
Kernel methods are best known for the popular method Support Vector Machines which is really a constellation of methods in and of itself.
不同分类核函数的相互比较分析表明，线性核函数最适于植物病害的分类识别。
The comparison of different kernel functions for SVM shows that liner kernel function is most suitable for shape recognition of plant disease spot.
然后分析了多结点样条插值方法的逼近精度、正则性、插值核函数的频域特性。
Then the accuracy of approximation, regularity of many-knot splines interpolation method and frequency property of the interpolation kernel are worked out for comparison.
核函数参数和误差惩罚因子的选择对支持向量机模型（SVM）的精度有较大影响。
The selection of the kernel function parameter and error penalty factor affected the precision of the support vector machine (SVM) significantly.
本文给出了在跨音速流动中，计算三维机翼非定常载荷分布的核函数法和计算结果。
A kernel function method and the results of computing unsteady load on three-dimensional wings in transonic flow are presented in this paper.
不同分类核函数的相互比较分析表明，径向基核函数最适合于葡萄病害的分类识别。
The comparison of different kernel functions for SVM shows that RBF kernel function is most suitable for recognition of grape disease.
不同分类核函数的相互比较分析表明，径向基核函数最适合于葡萄病害的分类识别。
The comparison of different kernel functions for SVM shows that RBF kernel function is most suitable for recognition of grape disease.
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