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kernel function相关的网络例句

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与 kernel function 相关的网络例句 [注:此内容来源于网络,仅供参考]

The representative points of positive case support vectors and negative case support vectors in sample set are defined, the measure and eigenfunction between sample x and y allusiveness from initial space to eigen space under a kernel function k are researched, and a correlative theorem with demonstration is given.

定义了样本集中的正例支持向量和反例支持向量的代表点,研究了原始空间样本x,y在核函数k映射下在特征空间中的x,y映象之间的度量和特征函数并给出了相关的定理及证明。

Based on his result, Amari proposed a method of modifying a kernel function using it抯 geometric character. Which can improve the respective of SVM.

根据他的结果,Amari在同年提出了一种利用核函数的几何性质对其进行修正的方法,以提高SVM的分辨率。

The asymptotic expansion of the kernel function K for large z andgeneral formulae of its coefficients have been derived and used in calculatingresistance.

本文的主要贡献在于推导了 z为大值时核函数 K的渐近展开式及其系数的通项公式,并用于阻力计算中。

Theory and experiment have verified that autocorrelation kernel function is correct and effective.

本文中,我们研究和分析了核函数与非线性映射以及所映射空间之间的关系。

With SVM,there is no a uniform mode to choice SVM's kernel function and its parameters.

在SVM学习中,对SVM的核函数及其参数的选择还没有形成一个统一的模式。

This method constructs a quasi-conformal transaction with the sech function. It improves the respective of SVM by modifying the polynomial kernel function. The text emphasizes on the parameter analysis of quasi-conformal transaction.

该方法用双曲正割函数构造拟共形变换函数,对多项式核函数进行修正来提高SVM的分辨率,并侧重于拟共形变换函数中的参数分析,确定了参数的取值方法,缩小了参数选取的范围。

We all know that mother wavelet function can create waveletframe, and the kernel function with wavelet frame can build a group of basis insquare integral space only by translation.

由于母小波函数可以生成小波框架,我们用小波框架来构造核函数,而采用该核函数的SVM,能够逼近平方可积空间中的任意函数。

A generic face mesh was deformed and the texture image was computed using image-based modeling technique. The deformed mesh was then convolved with a kernel function to generate a convolution surface, and wrinkles were generated by modulating the surface with a designed profile function.

运用基于图像的建模方法将通用人脸网格变形并计算出纹理图像,变形后的网格再与一个核函数卷积生成卷积曲面,通过轮廓函数调整曲面的形状而产生皱纹。

In this paper, the image intensity surface for the neighborhood of every pixel is well-fitted by the Least Squares Support Vector Machine, and the gradient and the zero-crossing operators are deduced from the LSSVM with the Radial Basis Function kernel function, as an example.

摘要该文利用最小二乘向量机对原始图像每一像素的邻域作灰度曲面的最佳拟合,并以径向基核函数为例导出了图像的梯度算子和零交叉算子。

However, some problems of it, such as the expenditure problems in training SVM, the SVM's generalization function, the selection of the kernel function etal, have not been well resolved in the long run.

但是,支持向量机的训练费用、泛化性能以及核函数的选择等关键问题,长期以来都没有得到彻底的解决,这使得支持向量机的理论优越性没能够在更广泛的实际应用中凸现出来。

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