样本分布

样本分布符合HardyWeinberg平衡。

The sample distribution was accorded with Hardy Weinberg principle .

本文中，设AR（p）序列的样本分布为椭球分布，AR（p）序列的噪声为椭球白噪声。

In this paper , suppose that the sample distribution of AR ( p ) sequence is an elliptical distribution , or the white noise of AR ( p ) sequence is elliptical white noise .

负相协样本分布函数的递归型核估计基于XML的分布式数据库递归查询优化

A Recursive Distribution Function Estimator Under NA Samples One of Distributed Recursive Queries Based on XML

基于Logistic总体Ⅱ型截尾样本分布参数的极大似然估计

Maximum Likelihood Estimation Based on Type ⅱ Censored Sample Parameters of a Logistic Distribution

NA样本分布估计的一致强相合性

Uniformly Strong Consistency of Nonparametric Distribution Function Estimators

为研究Logistic分布的拟合优度检验问题等，讨论了基于Logistic分布Ⅱ型截尾样本分布参数的极大似然估计。

In order to study the goodness-of-fit tests of logistic distribution , the author discusses the maximum likelihood estimation based on type ⅱ censored sample parameters .

在RBF网络基函数中心选取时，基于样本分布特点，采用简单有效的均值法，同时为了增加了神经网络权值学习的鲁棒性和快速性，将H∞鲁棒滤波用于网络的权值调整中。

Then RBF neural network , with its radial function chosen by average method and weights adjusted by H ∞ robust filter , is applied for changing the pose angles to ensure the robustness and real-time computing .

此外，传统的Fisher线性判别多类类间散度是通过每类的均值和总均值之间的差值计算的，该算法没有考虑样本分布的局部性。

The traditional Fisher discriminant , measures the between-class divergence by the distance between sample mean in every class and total mean , it does not consider the locality of data distributions .

当处理同类别多区域样本分布问题，例如变标签问题时，距离判别、Fisher判别、k-近邻分类、分段线性分类等统计分析方法遇到困难。

The statistic approaches , for example , distance , Fisher , k nearest neighbor , wise linear classifiers , fail to solve multi regional distributions such as the alternate table problems .

试验结果表明平面赤足迹的形态特征能够反映人体身高、体重等生理特征，83.65%的样本分布在身高回归线的±5cm范围内；

Experimental results show that people 's height and weight can be predicted roughly by shape features of planar barefoot impression proposed in this thesis .

通过与SPF算法进行对比实验发现，该算法的采样位置相对固定，样本分布均匀，可以实现粒子多样性的要求。

Compared experiments with the SPF algorithm showed that the sampling location of the algorithm is relatively fixed , the sample distribution is uniform and it can be achieved the requirement the particle diversity . 3 .

引入Bootstrap重采样技术，根据少量历史数据获得合格率分布的非参数化数值解，作为先验条件与当前样本分布相结合，通过贝叶斯方法对尺寸质量参数进行估计。

A Bootstrap resampling method was implemented to build the nonparametric prior empirical distribution of pass-rate , then Bayesian statistical algorithm is used to infer the pass-rate by combining prior empirical distribution with the currently observed distribution .

针对测量结果标准不确定度因测量数据少、数据样本分布不明确而难以评定的问题，提出一种基于灰色系统理论的非统计评定方法，其参数由灰色模型GM（0,2）和数值仿真确定。

Aiming at solving the problem of standard uncertainty evaluation caused by the lack of data or definite probability distribution of a sample , a grey evaluation method of standard uncertainty is proposed whose parameter can be calculated via numerical simulations and GM ( 0,2 ) .

通过分析训练样本分布、恰当地设计适应度函数，运用优进遗传算法（EGA）实现参数寻优。

The correlative technology of the optimization method includes analyzing the distribution of training sample data and designing appropriately the fitness function and optimizing parameters by eugenic evolution genetic algorithms ( EGA ) .

对Kohonen网络提出了如下改进：根据样本分布进行距离函数的选择，用邻域函数来改善自组织竞争性，提出基于全局映射的网络训练方法和基于二次映射的网络收敛方法；

Some improvements in Kohonen arithmetic is brought up , such as using neighbor function covering for neighbor circle to ensure fair competition , network training method based on total mapping and network constringency method based on Double mapping .

CVFDT适合处理流动数据，随数据样本分布的变化更新模型，并能处理概念漂移。

CVFDT is capable of processing dynamic datasets , coping with concept drift and updating the model catering to incoming data .

本文提出了空间逐步寻优的数据挖掘方法（SOMM），是在遗传算法寻优理论基础上，根据知识参数化样本分布函数，来逐步分离样本空间，获得样本空间的树状的层状分布结构；

This article presents a new modal named Stepwise Optimization Making Modal ( SOMM ) which is used to dig out the tree-like hierarchical distribution structure from feature space based on the Genetic Algorithms Optimization Theory and the knowledge fused distribution functions .

基于增益的数据样本分布描述方法

Describing method of the distribution of data-sample based on Gain

这将导致当输入样本分布结构呈高度非线性时，其分类能力下降。

It fails as the distrubution of input patterns becomes highly nonlinear .

结果：样本分布比较合理，代表性较好；

Results : Sample data distributes reasonably , has good representative trait ;

相协样本分布函数光滑估计的正态逼近速度

Uniformly asymptotic normality of the smooth estimation of the distribution function under associated samples

相依样本分布函数、回归函数的非参数估计的强相合性

Uniform strong consistency of nonparametric estimation of a distribution function and a regression function under Dependent Samples

研究结果表明，在样本分布和量化级数不变时，泛化均方差和学习均方差是权调整率的非增函数。

Research results indicate that generalization ability and learning accuracy are the non decreasing function of weight adjusting ratio .

把样本分布信息融于特征提取过程将有助于提高特征的分类能力。

Incorporating the sample distribution information into the process of feature extraction is beneficial to promote the classification performance of features .

当子分类器均受训练样本分布影响较小，组合结果也具有较好的稳定性。

If the distribution of training samples only had little influence on the sub-classification , the combined classifiers would have stable performances .

为了获得较高的推广能力，必须让样本分布广的类处于二叉树的上层节点，才能获得更大的划分空间。

To maintain high generalization ability , the most widespread class should be separated at the upper nodes of a binary tree .

利用小波变换可以对样本分布曲线进行光滑化处理而得到近似分布曲线，该曲线平滑了样本分布曲线上一些变化较大的区域，可以去除噪声干扰。

After wavelet transforming , the distributing curve of these swatches will be smoothed . And this will remove the disturbance of the noise .

从中国上市公司的行业分析入手，通过描述性统计方法确定样本分布特点，选择非参数检验方法对资产结构行业间差异进行分析，得出存在行业差异的结论。

Beginning with the analysis on industry characters of listed company in China , the paper estimates distribution nature of samples by descriptive statistics measure .

本文对与一些多样性测度的均值、方差的估计和假设检验以及大、小样本分布等有关的问题作了综述。

The estimation of means and variances , hypothesis testing , and large or small sample distributions for some diversity measures are reviewed in this paper .

根据样本分布的不规则性，引入了子类的概念，使每个类由若干子类覆盖，每个类生成一个单独的网络。

As the samples usually distributes irregularly , authors use the conception called subclass , let every class be represented by several subclasses and generate an improved RBF network .