kullback-leibler divergence

By the use of Kullback-Leibler divergence in information theory, we can gain further distinction between objects and shadows.

利用信息论中的交叉熵进一步区分目标与阴影。

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Based on Kullback-Leibler divergence between continuous distributions, we develop a novel nonlinear correlation clustering algorithm. 4.

基于衡量连续分布之间距离的Kullback-Leibler Divergence，我们提出了一个新的非线性相关聚类算法。

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Random Generalized Cookie-Cutter Set and Applying Kullback-Leibler Divergence Distance to Construct Vertebrate Phylogeny

随机广义Cookie-Cutter集与利用Kullback-Leibler散度距离构造脊椎动物系统发育树

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It measures Kullback-Leibler ( KL) divergence between two probability distributions.

它测量的是两个概率分布之间的Kullback-Leibler（KL）距离。

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This paper reviewed the Bayesian robustness of classes of ε-contaminated priors. The criteria for measuring its robustness are kullback-leibler divergence of the posterior distribution for the classes of-contaminated priors and it's curvature.

考查了ε-代换类先验的贝叶斯稳健性，衡量其稳健与否的标准是先验为ε-代换类的后验分布的kullback leibler散度和它的曲率。

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This paper modifies the query-by-committee ( QBC) method of active learning by combining vote entropy and kullback-leibler divergence for learning TAN classifier to model telecom clients' credit classification.

结合委员会成员投票熵和相对熵，改进了基于委员会选择算法（QBC）的主动学习，并应用基于该算法的主动贝叶斯网络对电信客户信用风险分类进行建模。

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First, the blurred image be computed by statistical model, then the similarity between the images at different depths can be computed by this statistics and a very classical algorithm that is Kullback-Leibler ( KL) divergence.

本文首先运用统计模型对图像进行统计，然后根据统计模型，结合相对熵中的一个典型算法Kullback-Leibler散度来计算不同深度图像之间的相似度。

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Shadow elimination by chrominance distortion and local kullback-leibler divergence

基于色度畸变和局部交叉熵的阴影消除

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A New Model Selection Criterion Based on Kullback-Leibler Information Divergence and Geometric Curvature

一种基于广义KL距离和几何曲率的模型选择准则

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The connections between Shannon mutual information, Kullback-Leibler divergence and Shannon inequality are investigated. Based on these connections and inequality theory, a new concept of generalized divergence is proposed, and a corresponding definition is given.

分析了Shannon互信息、Kullback-Leibler距离和Shannon不等式之间的相互关系，并根据不等式理论，提出了广义距离度量的新概念及其定义。

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Chapter 7 is the extension of Chapter 2, in which the convergence of the Kullback-Leibler ( KL) divergence based BSS algorithm is analyzed and from which the convergent areas are obtained.

第七章是在第二章基础上的扩展与延伸，我们主要对基于Kullback-Leibler（KL）散度（也称为I-散度）的NMF算法的稳定收敛进行了分析并获得了算法的收敛域。

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Kullback-Leibler's Divergence between Two Multivariate t-Distributions

两个多元t-分布之间的Kullback-Leibler距离

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