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Theoretically how to choose Kernel function remains unsolved. 在理论上，怎样选择核函数，还是一个未解决的问题。
Super advertising shield and attached a crucial handoff IE kernel function. 附带超强的广告屏蔽和IE内核一键切换功能。
Kernel function is applied in PCA and DDA, so the linear discriminance method is dislinearized. 在主分量分析方法以及线性判别式方法中,引入核函数,使得原来的线性判别方法非线性化。
For this reason, several important properties of kernel function are discussed on the basis of support vector machine. 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。
The kernel function is modified by means of a conformal mapping, which makes the kernel function data-dependent. 利用与数据有关的保角映射，使核函数具有数据依赖性。
When the Gaussian kernel function width is 2.0, RVM method possesses more perfect prediction performance. 当高斯核函数的宽度值取为2.;0时，相关向量机方法具有较为理想的预测效果。
The kernel function can be unit hydrograph of deterministic system or time series model of stochastic system. 通常核心函数可为定率系统之单位历线或序率系统之时间序列模式。
Geological information at different depth was described by introducing a mixed window kernel function to SVM. 通过引入窗口核函数，准确地反映了不同深度的地质信息。
The algorithm solves nonlinear regression problems mainly through inductive-insensitive loss function and kernel function. 这里提出了核学习技术在储集层非均质特性描述中渗透率参数预测的新用途。
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。
This paper proposes an improvement of LBF model，which utilizes a new kernel function instead of Gaussian kernel function. 提出了一个改进的LBF模型，它使用一个新的核函数代替高斯核函数。
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。
A signal and noise separator based on SVM whose kernel function is Gauss function was used to distinguish the target signals from noises, and enhance the SNR by removing noises. 采用以高斯函数为核函数的SVM所构成的信噪分离器，对信号和噪声进行识别和分离，从而消除噪声，得到高信噪比的超声回波信号。
The experiment results show that,when employing the same kernel function,the self-adaptability and forecasting accuracy of FFLSSVM is superior to SVM and LSSVM. 实验结果表明:FFLSSVM比由相应核函数构成的SVM、LSSVM自适应性强、预测精度高;
Recently，a new SVM kernel function GLDS，has shown better performance than conventional SVM kernel.This paper mainly introduces a method WCCN to process the SVM input vectors. 引入类内方差归一化（WCCN）方法来处理SVM的输入特征向量，并和GLDS核相结合，提出一种基于类内方差归一化和SVM的说话人识别方法。
A novel approach is discussed to choose the bandwidth for kernel density estimation.Based on the Gaussian kernel function, the recursion formula of bandwidth is derived. 摘要在未知总体分布的情形下，给出密度核估计中选择窗宽的一个新方法，并在选取高斯核的情形下，推导出计算窗宽的递归公式。
Some basic SVMs are constructed by adopting different kernel function or parameters and then the final prediction is obtained through aggregating the results of these basic SVMs. 提出一种基于SVM集成的核选择方法，利用不同的核函数构造子SVM学习器，然后对子学习器的预测结果集成。
This paper presented a lifting wavelet to construct a new wavelet function which could be used as an allowable kernel function for support vector machine(SVM). 摘要采用提升小波方法构造出一种满足双正交的小波函数，并将这种小波函数作为支持向量机的核函数；
In this paper,an SVM-based approach applied to predict steel quenching degree is presented,and the effects of selecting kernel function on SVM modeling are also analyzed. 本文提出了基于支持向量机模型预测钢淬透性的方法,并分析了核函数的选择对支持向量机建模的影响。
The algorithm introduces twice training of Riemannian geometry’s modifying the kernel function as well as a forgetting factor to decrease the the number of support vectors. 考虑到简化算法模型和降低计算复杂度，该算法引入遗忘因子和使用黎曼几何修改核函数的二次训练方法减少了算法的支持向量数目。