在模式识别、回归估计、概率密度函数估计等方面都有应用。
In pattern recognition, regression estimates, the estimated probability density function, and other aspects of application.
提出了一种新的类条件密度函数估计的PNN模型及其算法。
A novel PNN model with training algorithms is proposed for class conditional density estimation.
目前，主要应用在模式识别、回归估计、概率密度函数估计等方面。
Now it is applied in Pattern Recognition, regression estimate and probability estimate.
文中主要介绍了利用误差分布律概率密度函数定权来进行抗差估计的一种方法。
An introduction is Made of the method of using the weight of probability density function of error distribution law to conduct anti-error estimation.
讨论了在适当条件下，密度函数核估计的一致强相合性。
Under certain conditions, We discuss the uniform strong consistency of kernal estimator for the density function.
推广后的定位方法，可根据具体的目标定位场合，灵活选择核函数对样本点进行核密度估计。
Using this method, kernel function could be flexibly chosen to estimate sample point's density values according to different locating application scenes.
该算法运用多层感知器估计训练样本的分布函数，然后求导得到概率密度。
SLC uses a multiplayer network to estimate the distribution function of the training samples and obtains density by taking derivative.
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。
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.
支撑矢量机是一种普适的算法，已经广泛地用于模式识别、回归估计、函数逼近、密度估计等方面。
SVM is a kind of general learning algorithms, which has been widely used in pattern recognition, regression estimation, function approximation, density estimation, etc.
本文提出了利用一维核函数构造多维密度函数一个新估计的方法。
In this paper, a new kernel estimator of multivariate density is proposed by using a univariate kernel function.
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。
This method USES kernel density estimation model to construct the approximate density function, and takes hill climbing strategy to extract clustering patterns.
回归函数、密度函数等的样条估计具有优良的统计性质。
The spline estimates of regressive function, density function and so on have very good statistical properties.
由极大似然估计可以得到单因子利率模型的边际密度函数。
The marginal densities of single-factor interest rate models can be obtained by maximum likelihood estimation.
但是这种方法也有不足之处，就在于它对模型有一些弱的假定点估计依赖于误差因子与模型参数的假定，密度估计依赖于误差因子特征函数的假定。
The disadvantages were that this method was based on assumptions on the model: point estimation based on parametric assumption and some properties of error components.
该方法通过直接在可接受域上对性能的联合概率密度函数进行积分获得成品率的估计。
This method tries to integrate the joint probability density function on the acceptability region directly.
在不知道随机序列的概率密度函数的情况下估计出模型阶次及未知参数，使实际问题的解决成为可能；
When the probability density function of the random series is unknown, the order of the model and the unknown parameters are estimated to make possible the solution for an actual case.
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。
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.
概率密度函数的估计问题一直是数理统计中比较热门的问题，受到了许多学者的广泛关注。
The estimator of probability density function is always a hot topic in mathematical statistics, it received wide attention from many scholars.
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。
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。
About multivariate statistical process, three methods are introduced: Principal Component Analysis, Partial Least Squares, Kernel Density Estimation.
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