距离函数

词组短语

distance function

与距离函数相关的网络例句 [注：此内容来源于网络，仅供参考]

For both Gram-Schmidt method and adjoint matrix method of MTS, multicollinearity is solved by improving Mahalanobis distance function.

不管是马氏田口的施密特正交化法，还是伴随矩阵法，都是通过改进马氏距离函数来解决强相关问题。

Decision rule chain and distance function of decision rule are proposed, by which the order of decision rules are explained. The group scale's effect on decision quality and the marginal contribution of decision-makers are analyzed. It's pointed out that the increase of the scale will enhance the decision quality with the optimal decision rule applied. Otherwise it is likely to weaken the decision quality. It's proved that for homogeneous group with simple majority rule applied the marginal contribution of decision-maker will decrease while the scale increases or the decisional competencies of other decision-makers increase; 3.We study the group decision-making with a leader in it. It's proved that the existence of the leader impairs the decision quality when the group applies the optimal decision rule.

研究了决策者的决策能力不确定情况下的群体决策，给出了最优决策规则，指出其仍然是广义加权多数规则；提出了决策规则链和决策规则距离函数的概念，利用这些概念解释了群体决策规则的序现象；分析了群体规模对决策质量的影响、决策者决策能力的边际贡献，指出当群体应用最优规则时，群体规模的增大会提高决策质量，否则群体规模的增大可能会削弱决策质量；证明了对于应用简单多数规则的同质群体，随着其规模的增大，决策者决策能力对决策质量的边际贡献会减小；随着其他决策者决策能力的提高，决策者决策能力对决策质量的边际贡献也会减小； 3。

In this paper, we propose a new method for clustering without distance functions.

在本文中，我们提出了一种新的无需距离函数的聚类方法。

In the paper,the theory of production frontiers and its non-parametric measurement ap～proaches are used to figure out the input compressing productivity index,Malmquist productivity index based on the input oriented non-parametric production frontiers .The technology efficiency and thedis～tance function and their nonparametric models anr introduced on the conditions of Constant Return to Scale and input strong disposability The Non-pparametric measurement approaces and the decomposition of Malmquist prisuctibity index are put forward

孙巍文采用生产前沿面理论及其非参数方法，对假定产出确定条件下投入压缩的生产率指数基于投入前沿面的Malmquist生产率指数进行了理论与测度方法研究，通过规模收益不变且要素自由，C，S，前沿面条件下的技术效率，距离函数及其非参数模型的分析，本文给出了基于非参数，C，S，投入前沿面的Malmquist生产率指数的非参数测度模型，并从技术变化和资源配置效率变化两个方面进行了分解

Measure of uniformity is essential for constructing uniform design. Based on the distance concept, one kind of new distance function-potential function is introduced and used to measure the uniformity of points distributed in a general convex polyhedron.

均匀性度量是构作均匀设计的基础，本文从距离概念出发，通过对称的方法，得到一种新的距离函数－势函数，并将势函数作为衡量任意凸多面体上布点均匀性好坏的准则。

But the clustering of authority: the distance function is not on many commonly used is that's the distance.

但是对于聚类的依据：距离函数的介绍却不是很多，常用的是明氏距离。

Some methods are discussed to speed up the K-MEANS clustering algorithm with Euclidean and Mahalanobis distance norm.

同时，归纳并整理了一整套基于欧氏距离和马氏距离的K均值快速算法，为了解决传统基于距离函数算法的样本顺序相关问题，提出了针对两类问题的顺序无关聚类算法。

On the basic that existing second-order sufficient condition for an isolated minimizer of order 2 for unconstrained multi-objective optimization, using the so called oriented distance from a point to a set, l-stable function and generalized second-order Peano direction derivative, some second-order sufficient conditions for an isolated minimizer of order 2 for constrained multiobjective optimization problems with l-stable functions are obtained.

在已有的无约束l-稳定函数多目标优化问题的二阶充分条件的基础上，借助定向距离函数和l-稳定函数的性质及引入的广义二阶Peano方向导数，进一步刻画了具有抽象约束的l-稳定函数的多目标优化问题的二阶孤立极小点的二阶充分条件。

The optimal scheme is gained by taking the generalized distance as objective function and the scheme performance requirements as constraint conditions.

该模型定义了方案功能载体间的广义距离，以广义距离函数作为方案组合优化的目标函数，以方案的性能要求作为约束条件进行优化并获得方案的最优解。

Quantifying similarity/difference between two objects plays an important role in many contexts The quality of the similarity/difference scores can be improved by considering the semantic information related to the features of objects A flexible semantic distance function called X Dist is proposed, which can utilize the semantic information to measure the difference between two objects based on a solution to the transportation problem from linear optimization With a ground distance function for single features being a metric, X Dist is also a metric This property is very useful for making searching efficient, but is not investigated in the previous research Moreover, the experimental results show X Dist can be as good as the previously studied similarity measures in nearest neighbor searching, discriminative power and computing speed

量化对象间相似性/差别的方法具有广泛的用途，利用相关的语义信息能够得到更好的量化结果提出了一个量化对象间语义差别的距离函数XDist，它基于线性优化中的运输问题模型和相关的语义信息量化两个对象之间的差别在量化特征的差别函数是度量的情况下，XDist是一个度量，在提高搜索的效率方面具有优势，弥补了以往研究的不足，而且实验初步表明，此函数在最近邻查询效果、差别分辨力和计算速度方面能与已有函数相媲美1 引言量化对象间相似性/差别的方法具有广泛的用途，例如数据挖掘中的聚类；信息检索中的相似文档查询；协同过滤中的相似用户查询；CBR中的相似案例查询集合是对象特征表示的常用结构，集合的元素是与对象特征相关的项，它们可以

与距离函数相关的网络解释 [注：此内容来源于网络，仅供参考]

coherence length：相干距离

coherence function相干函数 | coherence length相干距离 | coherence scattering相干散射

distance decision function：距离判决函数

03.447 特征编码 feature coding | 03.448 距离判决函数 distance decision function | 03.449 概率判决函数 probability decision function

distance circle：距离圆

distance between two points 两点间距 | distance circle 距离圆 | distance function 距离函数

distance function：距离函数

记忆基础推理法中有两个主要的要素,分别为距离函数(distance function)与结合函数(combination function). 距离函数的用意在找出最相似的案例;结合函数则将相似案例的属性结合起来,以供预测之用. 记忆基础推理法的优点是它容许各种型态的数据,

symmetric distance function：对称距离函数

symmetric difference 对称差分 | symmetric distance function 对称距离函数 | symmetric figure 对称图形

Kullback-Leibler distance function：距离函数

Kuder-Richardson公式 Kuder-Richardson formula | Kuiper统计量 Kuiper statistic | Kullback-Leibler距离函数 Kullback-Leibler distance function

distinct roots：相异根;不等根

距离函数 distance function | 距离点 distance point | 相异根;不等根 distinct roots

Mahalanobis' generalized distance Mahalanobis：广义距离

loss function 损失函数 | Mahalanobis' generalized distance Mahalanobis广义距离 | mail survey 邮送调査

random：随机函数

第二、小星体要出现不同的大小,有一种远距离的感觉. 用随机函数(random)就可以实现星体不同的大小. 第三、小星体要从左边不同的位置出现,而且每一个都不一样,这里也要用随机函数(random)来实现.

redout：施法距离 依赖 MFC函数库 资料库

QuickLoot 快速拾取 | RedOut 施法距离 依赖 MFC函数库 资料库 | Reputation 外交关系 依赖 MFC函数库 资料库