样本函数

词组短语

sample function

与样本函数相关的网络例句 [注：此内容来源于网络，仅供参考]

Secondly, for the same hash function, the sample with the minimum hash value is selected as the global sample and the local samples are combined at the center node to filter out the biased effect of duplicated updates.

然后，对于相同散列函数产生的样本选取具有最小散列值的样本作为全局样本，完成局部样本集在中心节点的合并，滤除在分布节点上的重复更新对样本偏斜的影响。

In chapter 2 we obtain existence and joint continuity of the local time foradditive Lévy process under some conditions and discuss the upper bounds andlower bounds for the Holder laws of local times processes,the probability of somerare events ralated to small fluctuation of the sample functions and sojourn times insmall balls.Finally some liminf's results about the local and uniform oscillationsof additive stable process are acquired.

第二章给出了可加Lévy过程的局部时存在和联合连续的条件，讨论了其局部时过程的〓律上界和下界，以及可加Lévy stable过程跟样本函数局部振动相关事件的概率和停留在小球上的逗留时问题，同时对可加Lévy stable过程的样本轨道的局部和一致振动证明了一些liminf型结果。

Based on Monte Carlo method,four different weighted function moment methods for Pearson Ⅲ distribution which were developed in China are first compared by using the evaluation standard considering unbiasness and effectiveness of parameters and design values.

利用统计试验方法，以参数及设计值不偏性、有效性为评价标准，比较了现行几种权函数参数估计法，结果与以理想样本还原准则为标准所得的结论截然相反，以不偏性、有效性标准的结论是数值积分权函数法，并未对马秀峰权函数法作出改进，还从理论上比较分析以上两种评价标准，结论是理想样本还原为标准来评判估计方法的优劣存在较大缺陷，建议今后尽量不要使用该标

By using testing functions to make comparative research on the precision of approximate models which were built by RBF under the condition of different sample amounts and different kernel function types, a conclusion was drawn: the model quality built by Reciprocal Multiquadric RBF as kernel function was highest when the sample amout was big, and the model quality built by Gaussian RBF as kernel function was highest when the sample amount was small.

运用测试函数对在不同样本数量与不同核函数形式下所建立的近似模型的精度进行对比研究，得出逆多二次函数与高斯函数作为核函数分别在大样本和小样本情况下精度最高的结论，并验证了径向基函数近似模型方法能够兼顾效率与精度。

Moreover, during the radar signal generation, static and dynamic characters of backscattering coefficient are set up, and time sampling of each clutter unit is computed with voltage character and power spectrum character, and the sampling signals are added in every distance gate to output time series. In addition, noise is modeled with a function of Gaussian stochastic process.

在雷达信号模拟中，建立了杂波后向散射系数的静态和动态特性，从杂波的幅度统计特性与功率谱特性入手，计算了每个杂波单元的时域采样，按照对应的距离门叠加各个距离模糊间隔上的回波信号并形成时间序列，并采用一个高斯随机过程的样本函数来表示噪声模型。

The traditional mean shift algorithm requires a symmetric kernel, such as a circle or a rectangle, and assumes that the target's scale is symmetric during the course of tracking. The pixels are used on the edge as the samples, about 5% to 10% of the total pixels in the kernel.

与采用对称核函数的均值迁移目标跟踪算法相比，采用目标边缘点作为核函数中的样本点，参与计算的样本点为核窗口中样本的5%~10%，使图像处理速度达到了50帧/s以上，满足了实时跟踪的要求。

The traditional mean shift algorithm requires a symmetric kernel, such as a circle or a rectangle, and assumes that the target's scale is symmetric during the course of tracking.

与采用对称核函数的均值迁移目标跟踪算法相比，采用目标边缘点作为核函数中的样本点，参与计算的样本点为核窗口中样本的5%～10%，使图像处理速度达到了50 帧/s 以上，满足了实时跟踪的要求。

Fisher criterion assumes that each sample has a unimodal,symmetric distribution for each class.However,when the training sample size is large and their distributions are multimodal or skewed,the definition of classical Fisher criterion can not correctly reflect the distribution of the data in the sample space,in that case,the sets of discriminant vectors are not optimal virtually.

Fisher准则函数的前提条件就是假设每类样本数据满足单峰高斯分布，即各类样本在模式空间的分布近似椭球状，但是当训练样本数据较多且呈多峰分布时，传统的Fisher准则函数并不能准确反映样本数据的分布，显然基于Fisher准则函数的线性判别分析得到的最优判别矢量集也不是最优的。

We introduce an accurate online-learning support vector machine algorithm, including incremental and decremental algorithm, which efficiently updates a trained regression function whenever a sample is added to or removed from the training set.

本文充分利用支撑向量机解的稀疏性并结合KKT条件，当向训练样本集中添加新样本时，调用&递增&算法修改原有回归函数来得到新回归函数；当从训练样本集中剔除旧样本时，调用&递减&算法修改原有回归函数得到新回归函数，即在线学习方法。

Course topics will be selected from: the general theory of processes, sample function properties, weak convergence, Brownian motion, diffusions, Levy processes, Markov processes, martingales, Gaussian processes and further topics.

课程论题将从以下选择：过程的一般理论，样本函数的特性，弱收敛，布朗运动，扩散，Levy过程，马氏过程，鞅，高斯过程及更深的论题。

与样本函数相关的网络解释 [注：此内容来源于网络，仅供参考]

average sample number function：平均样本数函数

average sample number curve 平均抽样数量曲线,平均样本数曲线 | average sample number function 平均样本数函数 | average sample size 平均抽样检验个数,平均样本大小

sample covariance function：样本协方差函数

sample covariance coefficient 样本协方差系数 | sample covariance function 样本协方差函数 | sample covariance matrix 样本协方差矩阵

peaks：双变量的样本函数

membrane mathworks标识语. | peaks 双变量的样本函数. | pltmat 在figure窗口显示矩阵的命令.

peaks：双变量样本函数

membrane mathworks标识符. | peaks 双变量样本函数. | pltmat 在figure窗口显示矩阵的命令.

sample covariance：样本协方差

"sample change compaction ","选样变更紧致法" | "sample covariance ","样本协方差" | "sample function ","样本函数"

sample dispersion：样本方差

sample covariance matrix 样本协方差阵 | sample dispersion 样本方差 | sample function 样本函数

sample function：样本函数

digestive apparatus 消化器官 | sample function 样本函数 | hull ventilator 车盘通风器

sample function：样本函数=>標本関数

sample for reference 参照样本 | sample function 样本函数=>標本関数 | sample gear 抽样齿轮

sample generalized variance：样本广义方差

样本个数|number of samples | 样本广义方差|sample generalized variance | 样本函数|sample function

simple sample：简单样本

估计器(Estimator)和随机过程(Stochastic Process)简单样本(simple sample)是指这样的样本(X1,X2,...,Xn),它的分量Xi,i=1,...,n是独立同分布的随机变量(向量)估计器设(X1,X2,...,Xn)为一个样本,它的一个与总体分布无关的函数(或向量函数)f(X1,