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At first,the negative data is divided into several parts accumulatively according to the geometric distribution of the training data.

该方法首先根据样本的几何分布,用迭代的方式把负样本分成若干部分与正样本线性可分的样本;然后用L-SVM对这些正负样本进行分类,得到若干个线性分类器;最后,将这些线性分类器顺次组合,构成级联分类器。

Events,Operation and Relation of Sets, Classical Probability, Geometrical Probability , Statistical Stability of a Frequency, Axioms of Probability, Conditional Probability, Total Probability Theorem, Bayes' Rule,Independent Events,Independent Repeated Trials, One Dimensional Random Variables, Discrete Random Variables, Distribution Function of a Random Variables , Continuous Random Variables, Normal Distribution, Distribution of a Function of a Random Variable, Multidimensional Random Variables, Joint Distribution Function, Marginal Distribution Function,Discrete Two—Dimensional Random Variables,Continuous Two—Dimensional Random Variables, Independent Random Variables, Distribution of Functions of Random Variables,Expectation,Variance, Covariance, Coefficient of Correlation, Bivariate Normal Distribution, Law of Large Numbers, The Central Limit Theorems, Sample and Population ,Chi—Squared, T and F Distributions , Sampling Distributions , Point Estimation , Interval Estimation , Testing Hypotheses , A Test of Significance for Parameters in a Single Sample From a Normally Distributed Population , A Test of Significance for Parameters in Two Sample From Normally Distributed Populations .

本课程的主要内容:概率的概念与运算、随机变量及其分布、随机变量的数字特征与极限定理、数理统计的基本概念、估计和检验的基本方法,随机事件与概率随机事件、事件的关系与运算、几何概率、统计概率等,条件概率、全概率公式、贝叶斯公式、事件的独立性、二项概率公式,随机变量的概念、离散型随机变量、随机变量的分布函数、连续型随机变量、随机变量函数的分布,多维随机变量及其分布函数、边缘分布函数、随机变量的独立性、二维随机变量函数的分布,数学期望、方差、协方差和相关系数、大数定律、中心极限定理,总体与样本, X 2-分布、 t-分布和 F-分布,统计量及抽样分布,假设检验的基本概念、单个正态总体参数的显著性检验、两个正态总体参数的显著性检验。

Central Limit Theorem -- States that distribution of sample means tends towards an approximate normal distribution.

中心极限定理--样本的均值近似服从正态分布。如果样本容量较大,那么其均值的分布越近似于正态分布。

This article estimates the extreme loss distribution with extreme value theory, estimates the shape parameter with HKKP estimator, which is unbiased to small sample data, selects the right threshold by selecting smallest mean square error of the estimated cumulative distribution function and empirical cumulative distribution function and based on the extreme value theory method, this article estimates the extreme loss distribution and the p-percentile operational loss under certain believe range for Chinese commercial banks, then calculates the operational risk supervise capital.

本文利用极值理论对中国商业银行操作损失极端值分布进行估计,针对尾参数估计的来用传统Hill佑计对小样本数据容易产生偏倚的情况,提出了采用Hill佑计的改进―小样本无偏估计的HKKP佑计来估计操作损失的尾参数,针对由于阈值确定不准确导致结果偏差大的情况,采用最小化估计的累计概率分布与经验累计概率分布平均平方误差的方法确定较精确的阈值,估计出给定置信水平下操作风险损失的分位数,从而使得中国商业银行操作风险监管资本的测定成为可能。

In POT model, it used the technique of parameter estimator stability, making up for the limits of the currently popularly-used Mean Excess Function e, and the threshold quantitatively selected by Kurtosis Method has also been achieved, and solving the problem the illustration method couldn't be applied to.

特别是,在BMM模型中充分地考虑了子区间极值一般极限分布与极值序列极限分布之间的关系,测度了受子区间长度影响的极值VaR;在POT模型中运用参数估计量稳定性法弥补了目前普遍采用的样本平均超出量函数e法的不足,针对一些图解法无法适用的问题,实现了峰度法对阈值的定量选取,并对指数回归模型法、子样本自助法、序贯法等定量法进行了分析探讨。

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。

Based on the order statistic theory,the interval estimation of sample extremes on large and small samples is given.

本文从次序统计理论出发,建立了该样本的极大值与极小值的区间估计算法,并对大容量和小容量样本的情况分别进行了讨论,获得了在小样本情况下可用母体均匀分布获得的结果来估计样本极值区间的结论。

The method includes the following steps: firstly, a spherical neighborhood of present sample points is established by using the geometric spherical-modelling theory and all the sample points contained in the spherical neighborhood are adopted as candidate neighbor points, thus not only preserving the effectivity of the dimension reduction capability when data sets are sparse but also getting the advantages of low-sensitivity to isolated points and good stability of the preserved topological structure; then a data relevance matrix more matching semantics can be obtained by relevance measurement based on route clusters to update the candidate neighbor points in the spherical neighborhood and optimize the regular neighborhood space of the present sample points, thus improving the phenomenon that the dimension reduction of sample sets provided with folded curved faces is apt to suffer the integrated-structure distortion in case of heterogeneous data distribution.

首先利用几何开球原理建立当前样本点的球状邻域,将包含在球状邻域内的所有样本点作为候选近邻点,不但能够保持在数据集稀疏情况下的降维性能的有效性,而且具有对孤立点敏感性不高、保留拓扑结构稳定性好的优点。然后利用基于路径聚类的相关性度量得到更符合语义的数据相关性矩阵,用来对球状邻域内的候选近邻点进行更新,优化当前样本点的规则邻域空间,改善了当数据不均匀分布时在带有折叠弯曲面的样本集上降维容易出现整体结构扭曲的现象。

Based on the previous fault possibility distribution kept within the sample mean and covariance matrix with forgetting factors,a strategy for constructing the target output of the new training sample set was ...

该算法通过带有遗忘因子的样本均值和样本协方差矩阵保存样本所包含的故障可能性分布信息,并在此基础上产生新增样本的目标输出,用于训练FBF网络,以实现故障分类边界的在线跟踪。

As a result, it is convenient for the recognition system to use the simple linear classifier to segment the sample space, so that the accuracy and distortion invariant ability can be easily achieved. The classification structure of a bifurcating tree is utilized to decompose the classification problem of multi-classes to the multi-stage classification of two classes, which simplifies the design of the classifier, and improves the searching efficiency as well. A gray-scale analysis based method for extracting features from optical correlation result is proposed.

提出了二叉树鉴别分类方法,在使用同类图象的多个典型畸变样本训练识别系统的基础上,利用鉴别分析方法优化各类图象的特征向量在样本空间中的分布,不仅有利于同时提高系统的准确性和抗畸变能力,而且便于采用简单的线性分类器来划分样本空间;利用二叉树分类结构,将多类样本的分类问题分解为多级二类样本的分类问题,不仅简化了分类器的设计,而且提高了搜索效率。

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