基于SVM理论的一种新的数据分类方法

基于 SVM分类器在模式识别问题中有独特的优势 ,本文通过对标准 SVM模型的改造 ,提出了一种新的简单的数据分类方法 .理论分析和实验表明 ,该方法与标准 SVM分类方法相比具有处理大规模数据识别的能力且保持较高的样本识别率 ,节省存储空间等优势 .     

一种通用的基于梯度的SVM核参数选取算法

核函数的选取是SVM分类器选取的核心问题.核函数的自动选取既可以提高分类器的性能,又可以减少人为的干预.因此如何自动选取核函数已经成为SVM的热点问题,但是这个问题并没有获得很好的解决.近年来对核函数参数的自动选取的研究,特别是对基于梯度的优化算法的研究取得了一定的进展.提出了一种基于梯度的核函数选取的通用算法,并进行了实验.     

截尾数据的非参数回归函数的核估计

基于｛（Ｘｉ，Ｚｉ，δｉ），１≤ｉ≤ｎ｝，我们建立了Ｅ（Ｙ│Ｘ＝ｘ）的估计ｍｎ（ｘ）和ｍ＾ｎ（ｘ），并证明了估计量的强弱相合，积分绝对误差的强相合与平均相合性。     

基于KMOD核函数的SVM方法在信用评分中的应用

本文介绍了支持向量分类机,并引入具有更好识别能力的KMOD核函数建立了SVM信用卡分类模型.利用澳大利亚和德国的信用卡数据进行了数值实验,结果表明该模型在分类准确率、支持向量方面优于基于RBF的SVM模型.     

随机损失数据下回归函数核估计的强相合性

在不完全数据下，本文得到了回归函数核估计的强相合性。这里所说的不完全数据是指子样数据按一定的随机律被删除。     

求解非线性函数零点的一种迭代格式

本文就求解相当广泛的一类函数的零点问题，建立了一种简单而有效的迭代格式，证明了迭代格式的收敛性并给出了几个实际例子．     

基于遍历函数型数据条件风险率函数的核估计

探究了在平稳遍历函数型数据下条件风险率函数的非参数核估计问题,本文基于N-W核估计的方法,构造响应变量Y在给定函数型解释变量X下的条件风险率函数非参数核估计,在一定条件下获得条件风险率函数非参数估计的偏差表达式.     

基于相依函数型数据非参数回归函数的稳健核估计

本文研究了基于相依函数型数据非参数回归函数的核估计.利用稳健的方法,在一定条件下获得了与i.i.d.场合下类似的估计量的几乎完全收敛速度,推广了现有文献中的相关结论.     

分布自由的回归函数核估计的相合性

在合适条件下，我们获得了基于完全和截尾数据回归函数核估计的逐步相合性，所获的结果对于所有Ｘ的分布μ均成立，因此是分布自由的。     

回归函数核估计的收敛速度

本文在P≥1的条件下,给出了回归函数m(x)的核估计m_n(x)的若干种p阶平均收敛速度,改进并推广了文献[1]及[2]中的若干结果。     

An Imputation Method for Missing Data in Compositional Based on Epanechnikov Kernel

Kernel function method has been successfully used for the estimation of a variety of function. By using the kernel function theory, an imputation method based on Epanechnikov kernel and its modification were proposed to solve the problem that missing data in compositional caused the failures of existing statistical methods and the k-nearest imputation didn't consider the different contributions of the k nearest samples when it used them to estimated the missing data. The experimental results illustrate that the modified imputation method based on Epanechnikov kernel get a more accurate estimation than k-nearest imputation for compositional data.     

一种基于高斯核的广义数据深度

本文提出了一个具有自适应等深面的广义数据深度函数.针对目前数据深度函数的中心区域都是凸的,造成无法有效处理具有ItrJ形结构数据的问题.本文从样本深度的角度,利用高斯核特征空间最小球原理,提出了一种广义的数据深度.它具有正交、尺度、平移不变性,满足弱单调性和无穷远处为0的性质.相比现有数据深度而言,它对凹形结构的数据具有更好的深度解释.更为重要的是它的计算比较简单,从而保证了它的实用性.     

An Iterative Variant of the Degenerate Kernel Method for Solving Fredholm Integral Equations

An iterative variant of the classical degenerate kernel method for solving Fredholm integral equations of the second kind is presented and its convergence properties are studied.     

Novel Linear Image Denoising Approach Based on a Modified Gaussian Filter Kernel

A Gaussian noise reduction technique for grayscale images is proposed in this article. It uses a modified Gaussian filter kernel based on a hyperbolic second-order equation. The introduced mathematical model differs from the classic Gaussian model provided by the heat equations, by a localization property. Our filtering approach reduces the amount of Gaussian noise and also enhances the image contrast. Some image denoising experiments that prove the effectiveness of the proposed method are also described in this article.     

Gradient Estimate of the Heat Kernel on Modified Graphs

We obtain a condition on the modification of graphs which guarantees the preservation of the Gaussian upper bound for the gradient of the heat kernel.      

基于方向数据的核密度估计的对称检验的大偏差

设$f_n$是基于核函数$K$和取值于$d$-维单位球面${\mathbb{S}}^{d-1}$的独立同分布随机变量列的非参数核密度估计. 我们证明了若核函数是有界变差函数, 随机变量的密度函数$f$是连续的和对称的, $\{\sup_{x\in {\mathbb{SS}}^{d-1}}|f_n(x)-f_n(-x)|,n\ge 1\}$的大偏差原理成立.     

一种基于Bhattacharyya核的SVM

包括图像识别在内的很多应用领域里,把单个样本表示成向量的集合的形式是很自然的想法,利用一个合适的核函数我们可以把这些向量映射到一个更高维的Hilbert空间,在这个高维空间里用Kernel PCA方法找到样本的高斯分布族,这样就可以把样本上的核函数定义成它们所服从的高斯分布密度函数的Bhattacharrya仿射.这样得到的核函数具有比较好的性质,比如说在各种变换下有稳定性表现,从而也说明了即使还有别的表示样本的方法,用向量集合的形式来表示单个的样本也是具有合理性的.     

纵向数据的有效秩推断基于修正的Cholesky分解

本文基于修正的Cholesky分解提出新的方法估计纵向秩回归的组内协方差矩阵,进而提出新的无偏估计函数改善不平衡纵向数据的估计效率.在一些正则条件下,建立了所提估计的渐近正态性.进一步,提出稳健的秩得分检验统计量对回归系数做假设检验.模拟研究和实证分析表明所提方法能够获得高度有效的估计以及所提检验方法比存在的方法更好.     

Modified Iterative Runge-Kutta-Type Methods for Nonlinear Ill-Posed Problems

This work is devoted to the convergence analysis of a modified Runge-Kutta-type iterative regularization method for solving nonlinear ill-posed problems under a priori and a posteriori stopping rules. The convergence rate results of the proposed method can be obtained under a Hölder-type sourcewise condition if the Fréchet derivative is properly scaled and locally Lipschitz continuous. Numerical results are achieved by using the Levenberg-Marquardt, Lobatto, and Radau methods.     

An Iterative Sparse-Group Lasso

In high-dimensional supervised learning problems, sparsity constraints in the solution often lead to better performance and interpretability of the results. For problems in which covariates are grouped and sparse structure are desired, both on group and within group levels, the sparse-group lasso (SGL) regularization method has proved to be very efficient. Under its simplest formulation, the solution provided by this method depends on two weight parameters that control the penalization on the coefficients. Selecting these weight parameters represents a major challenge. In most of the applications of the SGL, this problem is left aside, and the parameters are either fixed based on a prior information about the data, or chosen to minimize some error function in a grid of possible values. However, an appropriate choice of the parameters deserves more attention, considering that it plays a key role in the structure and interpretation of the solution. In this sense, we present a gradient-free coordinate descent algorithm that automatically selects the regularization parameters of the SGL. We focus on a more general formulation of this problem, which also includes individual penalizations for each group. The advantages of our approach are illustrated using both real and synthetic datasets. Supplementary materials for this article are available online.