核函数

The principal result shows the potential application of this kernel function.

初步结果显示了该核函数的应用潜力。

According to the analysis of compression instability, a new kernel function is proposed which can remove the instability effectively.

本文在分析出现压缩失稳现象原因的基础上，提出了一种新的核函数，消除了压缩失稳现象。

An improved kernel function was applied in the identification of a welding process with typical nonlinear character.

用改进的核函数，对具有典型非线性特性的焊接过程进行辨识。

Based on the boundary of the ability of spread, bring forward the gist of choosing SVM kernel function.

基于支持向量机的推广能力，提出选择核函数的依据。

The sensibility of kernel function support size can be weakened effect...

方法的缺点是弱化核函数覆盖尺寸的影响是以增大计算量为代价的，需进一步改进。

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。

The convergence of Mean Shift is analysed with common kernels. Several conclusions are made which are meaningful for the expansion and application of this algorithm.

然后对常用的核函数用于算法时的收敛性进行分析，得到了几个对算法扩展和应用有意义的结论。

Oilcan is a kind of typical rigid target with big backscattering coefficient, which takes on point figure if the resolution of SAR image is not very high; these point targets to be recognized gather together, and form a target region with some distribution area; the center of the region appears as a beeline, and the speckle noise appears scattered, therefore, considering the different grey distributions between point target and speckle noise region and the space geometry distribution, these point targets can be recognized by this algorithm.

由于在高分辨率情况下，合成孔径雷达图像中油罐个体的散射分别体现为由若干高亮度点围成的椭圆，待识别油罐目标的模糊边缘呈不连续的椭圆状，因此从二值化后的SAR图像中识别出拟目标的不连续边缘，判断其是否满足目标边缘特性来识别出最终结果，采用该方法解决了这类高分辨率下椭圆状目标的识别问题。本文运用了基于Gauss核函数支持向量机的SAR图像典型目标识别方法。

The computational complexity of the algorithm is polynomial growing with the sample size and the solution is the maximal margin one in the high dimensional space.

其主要的内容是：当问题是线性可分时，给出一个求解最大间隔解的方法以及当问题不是线性可分时，提出利用一核函数将样本集映

Programming to derive fuzzy optimal solutions and fuzzy optimal classification function seta fuzzy set which value is fuzzy optimal classification function and member degree is λ(0≤λ≤ 1,therefore the linear fuzzy support vector machine is constructe...

对于非线性模糊分类问题，引入核函数，类似于线性模糊分类问题得到非线性模糊支持向量分类机。最后构造显示模糊支持向量分类机特点的模糊支持向量集取值为模糊训练点，隶属度为λ(0≤λ≤1的模糊集合。模糊支持向量分类机较好地解决了支持向量机中含有模糊信息的分类问题。

The split between the two groups can hardly be papered over.

这两个团体间的分歧难以掩饰。

This approach not only encourages a greater number of responses, but minimizes the likelihood of stale groupthink.

这种做法不仅鼓励了更多的反应，而且减少跟风的可能性。