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In this research, the kernel function is in the simplest form of inner product ,but the new model is applicable for all the kernel functions.

虽然本研究所采用的核函数是最简单的向量内积,但所提出的模型对于所有的核函数都是适用的。

Therefore it is of great significance to study the properties of kernel function of support vector machine.

因此研究支持向量机的核函数性质,对于寻找核函数有重要意义。

Combining the testing accuracy of different kernel functions, an optimal kernel function is obtained to solve this problem.

综合各种核函数的测试准确率,得到解决该问题的最佳核函数

Volterra kernel functions were expanded on an orthogonal discrete Laguerre function set to produce a reduced system model with fewer parameters.

讨论了Volterra核的Laguerre展开法,通过将Volterra核函数在一个正交的Laguerre函数集上展开,得到一个简化的系统模型,使得待辨识的参数显著减少。

In experiment, Gaussian Maximum Likelihood Classification and Radial Basis Function neural network are used for comparison.

支持向量机」可选用不同核函数,而且会因核函数选用的不同,而对分类成果造成差异。

The convergence of the mean shift procedure to the closest mode of the underlying distribution is proven, both for the Epanechnikov kernel and the general case of kernels with convex and monotonic decreasing profile. The smooth trajectory property of the mean shift is also demonstrated.

均值平移算法对于Epanechnikov 核函数的收敛性在本文中得到了证明,进而推出均值平移算法收敛的充要条件是核函数具有凸的、单调递减性质的轮廓;揭示了均值平移过程运动轨迹的平滑性。

In [1], the kernel method of nonparametric density estimation is used to estimate the score functions directly.

文献[1]利用非参数法概率密度函数估计的核函数方法,通过对源信号评价函数的直接估计,提出了一种基于概率密度函数估计的DEBBBS算法,成功地解决了非线性函数替代方法存在的不能分离杂系混合信号的问题。

The finite-gain stability of the controller was proved under the assumption that the kernel function was local Lipschitz in this open-loop inverse control system. The sufficient condition for that Gaussian kernel function to the local Lipschitz of either variable was given. The stability of the whole system was also proved under some proper assumptions.

针对此开环逆控制系统,在核函数为局部Lipschitz的前提下,证明了控制器是有限增益稳定的,并给出Gaussian核函数对任一变量的局部Lipschitz性的充分条件,在一定合理的假设下给出了控制系统的稳定性结论。

The used SVM kernel function can not approach to any object function in regression.

针对目前使用的SVM核函数在回归中不能逼近任意目标函数的问题,在支持向量机的核方法和小波框架理论的基础上,提出了LS-WSVM结构模型。

Experiment shows it is perfect to classify study sample itself by the kernel function in this thesis, and the generalization ability of it is more predominant than SVM of traditional one.

实验表明了本文的核函数对学习样本本身的分类良好,而且泛化能力优于传统的核函数的SVM。

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