• How to use density kernel estimation for outlier detection?

    如何使用密度核估计的孤立点检测?

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  • The distributions are obtained by kernel estimation methods using a Gaussian kernel with the optimal bandwidth chosen using the method proposed in Silverman (1986).

    西尔弗曼1986的研究提出的方法进行了最优宽度选择,进而通过高斯核心估计方法获得了这个分布。

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  • A nonparametric kernel estimation of the flood frequency with historic information is presented in this paper. The limitation in assuming a distribution type is overcome.

    本文提出了在有历史洪水情况下洪水频率的非参数核估计方法,克服了现行方法假定分布型式的局限。

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  • Considering the flaw of history simulation method, the paper adopts kernel estimation method to estimate parameters and their standard deviation, and then gives case analysis.

    考虑到传统的历史模拟方法存在的缺点,采用基于核估计的历史模拟方法获得估计值及其置信区间,并用实例进行了分析。

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  • The kernel estimation method analyzes the data distribution by not using the prior knowledge of data distribution. This method avoids the impaction of model and parameters estimation.

    核密度估计方法从数据样本本身出发研究数据分布特征,不利用有关数据分布的先验知识,避免了模型估计和参数估计的主观影响。

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  • The kernel function model selected by LOO estimation can approach the best value satisfactorily.

    应用LOO估算选择的核函数模型能够较好地逼近最佳值。

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  • The kernel function model selected by LOO estimation can approach the best value satisfactorily.

    应用loo估算选择的核函数模型能够较好地退近最佳值。

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  • This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.

    该方法采用核密度估计模型来构造近似密度函数,利用爬山策略来提取聚类模式。

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  • According to the statistic characteristic of covariance function, parameter estimation can be given for kernel function.

    由协方差函数的统计特征,可给出核函数的参数估计。

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  • The background samples are chosen by thresholding inter-frame differences, and the Gaussian kernel density estimation is used to estimate the probability density function of background intensity.

    通过相隔固定的帧差值阅值化得到背景样本值,并采用高斯核密度估计方法计算背景灰度的概率密度函数。

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  • In this paper, the estimation of coefficient functions in a varying-coefficients EV model are constructed by using kernel smoothing and generalized least square method.

    利用核函数法和广义最小二乘法给出了一般变系数ev模型系数参数的估计,得到了估计的强相合性。

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  • The Gaussian kernel mean-shift algorithm which is deduced from kernel density estimation has not been widely employed in applications because of its low convergence rate.

    由核密度估计推导获得的高斯核均值漂移算法因收敛速度慢在应用中效率不高。

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  • The first is kernel density estimation (KDE) detection based random walk ordering filter.

    第一种是基于核心密度估计检测的随机游走排序滤波器。

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  • Then the rice yield in Jiangsu is fitted by nonparametric kernel density estimation method.

    以非参数核密度估计方法拟合江苏稻谷的亩产量。

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  • To satisfy the need of video moving object locating in intelligent video surveillance scenes, video moving object locating technology based on nonparametric kernel density estimation is proposed.

    针对智能视频监控场合对视频运动目标定位的需求,本文提出了一种基于非参数核密度估计的视频运动目标空域定位技术。

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  • This paper introduced the selection principle and method about a reasonable kernel function and bandwidth based on the nonparametric kernel density estimation and kernel regression estimation.

    本文基于非参数核密度估计与核回归估计的基础上,介绍了合理选取核函数及窗宽的原则和方法。

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  • The kernel regression method now is the most popular non-parametric estimation method.

    核回归方法是比较常用的一种非参数估计方法。

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  • The kernel method of test equating has five steps, including presmoothing, estimation of test score probability, continuation, equating and calculation of standard error of equating.

    一般说来,核等值法有五个步骤,分别是前平滑处理、估计分数概率、连续化、等值、计算等值标准误。

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  • Mean shift based image segmentation algorithm is a kind of kernel density estimation based feature space analysis algorithm, and the nature of it is statistical optimization.

    均值漂移算法是一种基于核密度梯度估计的特征空间分析算法,其实质是一种统计优化过程。

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  • Infrared objects are represented by the intensity distribution, which is defined by the kernel-based density estimation.

    红外目标的描述利用目标区域的灰度分布,该灰度分布通过核概率密度估计建立。

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  • About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.

    多元统计过程介绍了三种主要的方法:主元分析法、偏最小二乘法和核函数概率密度估计法。

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  • About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.

    多元统计过程介绍了三种主要的方法:主元分析法、偏最小二乘法和核函数概率密度估计法。

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