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核函数

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Based on the Riemannian geometry theory, a novel conformal transformation was proposed and the kernel function was modified by the transformation in a data-dependent way.

以黎曼几何为理论依据,提出了一种新的保角变换,对核函数进行数据依赖性改进,进一步提高分类器泛化能力。

This function is a kind of orthonormal function, and it can approximate almost any curve in quadratic continuous integral space, thus it enhances the generalization ability of the SVM.

核函数不仅具有平移正交性,而且可以以其正交性逼近二次可积空间上的任意曲线,从而提升了支持向量机的泛化性能。

A new large-update interior-point algorithm for convex quadratic optimization based on the class of finite kernel functions is presented.

本文给出了凸二次优化问题基于一类有限核函数的新的大步校正内点算法。

One that is through different nuclear function choose and corresponding establishment of parameter collect , go on , return to to data-in.

通过不同核函数的选取以及相应参数的设置对输入数据集进行回归。此模型主要解决非线性回归模型的预测。

Multi-class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations.

与已有的多模式语音情感识别方式相比,利用高斯核函数的支持向量机的识别效果优于其他已有的方法。

A novel approach of binary component discriminant function is proposed by generalizing the original quadratic-form distance metric.

针对现有基于核函数的相关反馈方法处理样本不对称性时的缺陷,提出了基于超球面的支持向量分类方法。

The new IRS algorithm has greatly improved the approximation performance of the original reduced set method by embedding a new distance metric called image Euclidean distance into the kernel function.

其中,所谓IRS算法是指在精简集算法的核函数中嵌入一种新的距离度量——图像欧式距离——来改善其迭代近似性能,IRS可以有效地生成新的、位于类别边界附近的虚拟样本以增强给定训练集。

We obtain the explicit formulas ofBergman kernel function,Cauchy-Szeg?

得到了它们的Bergman 核函数、Cauchy-Szeg?

The foundations of support vector machines are introduced. Four mathematics models of support vector classifications including linearly hard margin SVM, linearly soft margin SVM, non- linearly hard margin SVM and non-linearly soft margin SVM are discussed.

摘 要:介绍了支持向量机的基本思想,依据是否引入核函数,是否具有惩罚因子,支持向量分类算法被分为线性分界面硬间隔、线性分界面软间隔、非线性分界面硬间隔和非线性分界面软间隔四类,并讨论了它们的数学模型。

SVM algorithm with Fourier kernel function is applied to detect candidate face area and its size.

并运用了Fourier域核函数的SVM算法检测出候选人脸区域及其大小。

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