基于密度核估计的贝叶斯网络结构学习算法

贝叶斯网络结构学习算法主要包括爬山法和K2算法等,但这些方法均要求面向大样本数据集。针对实际问题中样本集规模小的特点,通过引入概率密度核估计方法以实现对原始样本集的拓展,利用K2算法进行贝叶斯网络结构学习。通过优化选择核函数和窗宽,基于密度核估计方法实现了样本集的有效扩展;同时基于互信息度进行变量顺序的确认,进而建立了小规模样本集的贝叶斯结构学习算法。仿真结果验证了新学习算法的有效性和实用性。     

基于改进朴素贝叶斯的区间不确定性数据分类方法

基于Parzen窗的朴素贝叶斯在区间不确定性数据分类中存在计算复杂度高、空间需求大的不足。针对该问题,提出一种改进的区间不确定性数据分类方法IU-PNBC。首先采用Parzen窗估计区间样本的类条件概率密度函数(CCPDF);然后通过代数插值得到类条件概率密度函数的近似函数;最后利用近似代数插值函数计算样本的后验概率, 并用于预测。通过人工生成的仿真数据和UCI标准数据集验证了算法假设的合理性以及插值点数对IU-PNBC算法分类精度的影响。实验结果表明,当插值点数大于15时,IU-PNBC算法的分类精度趋于稳定,且插值点数越多,算法分类精度越高;该算法可以避免原Parzen窗估计对训练样本的依赖, 并有效降低计算复杂度;同时由于该算法具有远低于基于Parzen窗的朴素贝叶斯的运行时间和空间需求, 因此适合解决数据量较大的区间不确定性数据分类问题。     

分箱核密度估计的误差及其修正

棱密度估计的计算复杂度使其难以应用于大规模数据集的密度函数构造,采用分箱近似核估计是降低密度函数构造过程复杂度的有效手段.本文提出了一种修正简单分箱核估计误差的方法,该方法采用数据重心取代分箱中心作为数据的代表点,能够更准确反映数据的局部分布特征.经证明,该方法的拟合精度为D(δ4)(相对于窗宽),达到线性分箱核估计的水平.实验表明,修正的简单分箱核估计构造方法具有良好的时间效率和计算精度,能够运用于面向大规模数据集的聚类分析应用.     

可用带宽与瓶颈定位集成测量工具iPathneck

目前大部分的网络测量工具都是采用主动测量方法,但是由于大量的探测包注入会给网络带来流量负载并影响测量精度．如何在保证测量精度的前提下减小测量开销,成为值得关注的问题．提出的集成测量工具iPathneck在Pathneck工具的基础上采用速率模型提高可用带宽的测量精度,实现了可用带宽和瓶颈定位探测任务的归并,减小了测量开销并提高测量精度．实验表明：iPathneck可用带宽和瓶颈定位的测量准确性和算法时间收敛性都有所提高,任务归并能有效地减小测量开销．     

一种基于pathchar算法的改进的网络端到端带宽测量方法

在网络端到端带宽的测量中,Pathchar测量算法是目前网络链路带宽测量算法中最成功的算法之一,但是也存在不能对反方向路径上各段链路带宽进行测量、背景流量和逐跳误差累积影响等问题.本文提出一种改进的基于pathchar的测量方法较好的解决了上述问题,提高了测量的精度和抗干扰性.     

采用密度比估计的多窗口变点检测方法

针对基于密度比估计的时间序列变点检测方法受时间窗窗宽限制,识别变点类型单一的问题,利用和发展动态多重过滤算法MFA(multiple filtering algorithm),提出一种多窗口变点检测方法 mDRCPD(multiple window density-ratio change point detection)。将处理后的时间序列按多个时间窗进行适当划分,通过比较相邻时间窗数据的分布差异来识别变点,采用基于密度比估计的相对皮尔逊散度来度量不同时间窗数据分布的差异性;固定窗宽下寻找变点集,并按照MFA方法集成各变点集。模拟实验和实证分析表明,与基于密度比的单窗口变点检测方法相比,mDRCPD方法在多变点时间序列变点检测中绝对误差、召回率、F1得分等指标均有改善。将mDRCPD方法应用到COVID-19的传播进程分析中,通过对传播率的分段建模来刻画疫情的阶段性特点,评估国家政策在降低疫情传播速度上的效果。     

一种改进的基于核密度估计的DPC算法

快速搜索和找到密度峰DPC(clustering by fast search and find of density peaks)的聚类是一种新颖的算法,它通过找到密度峰来有效地发现聚类的中心。DPC算法的精度取决于对给定数据集的密度的精确估计以及对截止距离dc(cutoff distance)的选择。dc主要是用于计算每个数据点的密度和识别集群中的边界点,而DPC算法中dc的估计值却主要取决于主观经验值。提出一种基于核密度估计的DPC方法(KDE-DPC)来确定最合适的dc值。该方法通过引用一种新的Solve-the-Equation方法进行窗宽优化,根据不同数据集的概率分布,计算出最适合的dc。标准聚类基准数据集的实验结果证实了所提出的方法优越于DPC算法以及经典的K-means算法、DBSCAN算法和AP算法。     

基于包对测量的路径容量估计方法改进

测量样本的统计分析是基于包对技术的路径容量估计的关键.提出一种路径容量包对估计方法改进,将端到端路径视为离散控制过程的系统,以路径容量描述其状态.首先以包对探测方式连续发送一定数量的背靠背包对序列,获取足够的路径容量测量样本;再采用卡尔曼过滤算法对测量样本进行统计分析,以准确估计路径容量.建立了路径容量估计的滤波方程,给出了路径容量估计过程.实验表明,与pathrate算法相比,提高了估计的准确性并降低了测量探测量和测量时间.     

一种端到端可用带宽的测量方法

重点研究了网络端到端可用带宽的测量方法,分析了IGI和PTR算法的原理和局限性,将算法从单跳模式扩展到多跳网络,利用延时变更的概念,分析了探针包序列间隔变化与背罱流量的关系,以此估计背罱流量,并运用“相等区间”的方法确定最佳测量点,提高了可用带宽测量的准确性。     

基于Parzen窗法的贝叶斯参数估计

针对贝叶斯估计中所需的非规则概率密度函数, 提出用Parzen窗算法估计相关概率密度, 从而求解不同损失函数下的贝叶斯参数估计器. 实例分析中, 选择一组电阻测量值作为样本, 利用Parzen窗法计算出相应的概率密度函数, 最后用交叉验证法得出了该样本的最小绝对值误差参数估计器.     

一种基于核密度估计的图像边缘检测方法

进行图像边缘检测的算法有很多种,其中基于Sobel算子、Laplace算子、Canny算子等的图像边缘检测方法当属经典。但所提方法不同于这些差分算子方法,而是对灰度图像素进行小窗口区域的核密度估计,从而得到一幅核密度图,然后通过核密度图,选择出合适的带宽或阈值来控制图像边缘的检出。实验表明该方法可行且简单快速。     

Smoothed Bagging with Kernel Bandwidth Selectors

Recently, a combined approach of bagging (bootstrap aggregating) and noise addition was proposed and shown to result in a significantly improved generalization performance. But, the level of noise introduced, a crucial factor, was determined by trial and error. The procedure is not only ad hoc but also time consuming since bagging involves training a committee of networks. Here we propose a principled procedure of computing the level of noise, which is also computationally less expensive. The idea comes from kernel density estimation (KDE), a non-parametric probability density estimation method where appropriate kernel functions such as Gaussian are imposed on data. The kernel bandwidth selector is a numerical method for finding the width of a kernel function (called bandwidth). The computed bandwidth can be used as the variance of added noise. The proposed approach makes the trial and error procedure unnecessary, and thus provides a much faster way of finding an appropriate level of noise. In addition, experimental results show that the proposed approach results in an improved performance over bagging, particularly for noisy data.     

多分辨率差分图像非参数核密度估计方法仿真

针对多分辨率差分图像核密度估计阶段中,由于信息冗余与重复计算导致的估计结果准确率下降的问题,提出一种非参数核密度估计方法。利用硬件设备采集多分辨率视频序列,提取关键帧图像作为样本集。分割多分辨率的差分图像,形成由背景图像与前景运动目标两部分组成的初始模型。以该模型为基础构建Copula核函数,利用核函数的运算性能分别确定估计窗宽、方差和核密度公式,从而输出差分图像非参数核密度的估计结果。通过仿真得出结论:研究方法平均准确率为98.56%,与传统核密度估计方法相比提升了6.04%,证明此方法具有较高的应用价值。     

Accuracy of transformed kernel density estimates for a heavy-tailed distribution

Nonparametric estimation for the density of a heavy-tailed probability distribution is studied through transformation of initial observations. The accuracy of transformed kernel estimates with constant and variable window width in the sense of mean integrated squared error for different transformations is determined. Boundary kernel are designed for improving estimation on distribution tails. For a kernel estimate with variable window width, the mismatch method ensures a mean integrated squared estimation error close to the optimal error.Translated from Avtomatika i Telemekhanika, No. 2, 2005, pp. 55–72.Original Russian Text Copyright © 2005 by Markovich.     

Gaussian kernels for density estimation with compositional data

Common simplifications of the bandwidth matrix cannot be applied to existing kernels for density estimation with compositional data. In this paper, kernel density estimation methods are modified on the basis of recent developments in compositional data analysis and bandwidth matrix selection theory. The isometric log-ratio normal kernel is used to define a new estimator in which the smoothing parameter is chosen from the most general class of bandwidth matrices on the basis of a recently proposed plug-in algorithm. Both simulated and real examples are presented in which the behaviour of our approach is illustrated, which shows the advantage of the new estimator over existing proposed methods.     

Kernel Bandwidth Estimation for Nonparametric Modeling

Kernel density estimation is a nonparametric procedure for probability density modeling, which has found several applications in various fields. The smoothness and modeling ability of the functional approximation are controlled by the kernel bandwidth. In this paper, we describe a Bayesian estimation method for finding the bandwidth from a given data set. The proposed bandwidth estimation method is applied in three different computational-intelligence methods that rely on kernel density estimation: 1) scale space; 2) mean shift; and 3) quantum clustering. The third method is a novel approach that relies on the principles of quantum mechanics. This method is based on the analogy between data samples and quantum particles and uses the Schrodinger potential as a cost function. The proposed methodology is used for blind-source separation of modulated signals and for terrain segmentation based on topography information.     

Control-loop diagnosis using continuous evidence through kernel density estimation

While most previous work in the subject of Bayesian Fault diagnosis and control loop diagnosis use discretized evidence for performing diagnosis (an example of evidence being a monitor reading), discretizing continuous evidence can result in information loss. This paper proposes the use of kernel density estimation, a non-parametric technique for estimating the density functions of continuous random variables. Kernel density estimation requires the selection of a bandwidth parameter, used to specify the degree of smoothing, and a number of bandwidth selection techniques (optimal Gaussian, sample-point adaptive, and smoothed cross-validation) are discussed and compared. Because kernel density estimation is known to have reduced performance in high dimensions, this paper also discusses a number of existing preprocessing methods that can be used to reduce the dimensionality (grouping according to dependence, and independent component analysis). Bandwidth selection and dimensionality reduction techniques are tested on a simulation and an industrial process.     

基于自适应密度估计的系统参数辨识

针对噪声分布未知的ARMAX系统,提出了一种自适应非参数噪声密度估计方法,由估计误差动态调整高斯核函数的全局带宽和局部带宽,实现了未知噪声分布密度的自适应估计;通过极小化似然函数,给出了基于噪声密度估计的参数辨识迭代算法,分析了算法的收敛性并给出了算法收敛的充分条件.仿真结果表明本文提出的算法在系统噪声未知时具有较强的抗噪能力和良好的收敛性.     

DSP-based bottleneck bandwidth estimation

This paper presents a new passive technique for estimating the bottleneck bandwidth based on transferring the Gaussian kernel density estimation of the packets inter-arrival times to the frequency domain. The resulting spectrum contains information about the transmission time of the bottleneck link and can reveal information about multiple bottlenecks if they exist along the end-to-end path. The advantage of the technique is that it provides a model that can be manipulated by the digital signal processing methods and, unlike prior work in the area, it relies less on statistical methods. The proposed technique was validated using the ns2 network simulator [1] on several topologies and traffic sources. Further experiments were conducted to test the strength of the patterns between flows that share a bottleneck by applying K-means algorithm to cluster the average packet inter-arrival times of these flows. The paper also presents a set of results from real traffic experiments conducted in order to infer both the bottleneck bandwidth and the capacity of the path using a passive approach.