条件熵

通过分别对二步和k阶Markov预测器条件熵的计算，在理论上了证明了二步Markov预测器的可行性。

Its feasibility was proved by calculation conditional entropy of 2-step and k-order Markov predictor .

将Shannon信息论应用到SPECT成像领域，根据在SPECT系统中信息量、条件熵、互信息熵的意义，提出了评估投影数据完备性的原理和方法。

Shannon 's information theory was also applied to the SPECT imaging field . The physics was evaluated to analyze the conditional entropy and the mutual information .

有限非齐次马氏链的随和条件熵与Shannon定理的一个推广

The Random Conditional Entropy of Finite Non-homogeneous Markov Chains and an Extension of the Shannon Theorem

根据条件熵必不超过无条件熵这一结论，可知Context模型可以有效的减少信源的信息熵，从而减少图像编码的码长。

According to the conclusion that conditional entropy will be no more than the unconditional entropy , we can see the Context model can effectively reduce the entropy of the source of information , thereby reducing the code length of the image coding .

通过改进的模糊C-均值聚类算法对原始故障数据加以量化，采用正则条件熵进行诊断知识系统的统计约简，对不协调的诊断规则利用最小风险Bayes决策理论加以分析处理。

Regular conditional entropy is used to make the statistic reduction , and the modified fuzzy C-mean clustering algorithm to discrete the raw data . Bayes decision under minimum risk is utilized to deal with the contradictory diagnostic rules .

采用基于Huffman编码和条件熵的启发式评估函数，构造并实现了AO~算法。

In order to get the minimization of the cost of test sequence , an AO-based test sequence algorithm is implemented , whose heuristic evaluation function ( HFE ) is Entropy or Huffman code function .

然后分别计算Rossler-Lorenz与Rossler-Rossler混沌系统双向耦合的条件熵。

As an application , we calculate the conditional entropy of rossler-rossler and rossler-lorenz systems .

理论分析与实验结果同时证明，基于条件熵的特征选择算法可实现较高的故障检测率和较低的检测时间。（5）CCM的系统描述方法是一种经典的建立故障定位方程的方法。

The theory analysis and the experiments show that the conditional-entropy method can achieve high detection rate and low detection time . ( 5 ) CCM is a classical method to describe electronic systems , for building fault location equations .

其次，当考虑可逆动力系统间的因子映射时，我们证明了：正的条件熵不仅蕴含了纤维上真的渐近对的存在性，还蕴含了相对Mycielski混沌。

Then , when considering a factor map between invertible dynamical systems , it is shown that the positivity of relative entropy implies the existence of proper asymptotic pairs on fibers and relative Mycielski chaos .

基于新的条件熵的决策树规则提取方法

Rules extraction method of decision tree based on new conditional entropy

自相似随机级联过程中的条件熵和信息量

Conditional Entropy and Mutual Information in Random Cascading Processes

利用多尺度条件熵和记忆性度量估计图像无损压缩极限

Estimation of lossless image compression bounds based on multi-scale conditional entropy and memory measurement

一种基于条件熵的粗糙集连续属性离散化方法

A method of discretization of continuous attributes in rough sets based on conditional entropy

运用负值量子条件熵研究了双量子系统一类混合态的纠缠量度。

The identity of the results is proved by means of the fundamental quantum conditions .

采用条件熵的判定方法，提出了一种确定滑动窗口大小的算法。

By applying conditional entropy , a new algorithm for deciding sliding window size is proposed .

同时将基于条件熵遗传算法的特征抽取和支持向量机的分类模型进行联合优化。

At the same time , we combine the optimization of feature extraction and SVM classification model .

还提出了基于一般的集值函数的信息系统加权信息熵和条件熵概念。

And weighted infromation entropy and weighted conditional entropy of information system based on set-value function is presented also .

关于有限马氏链相对熵密度和随机条件熵的一类极限定理

A class of limit theorems for the relative entropy density and the random conditional entropy of finite Markov chains

简要地介绍了不确定性、信息熵、联合熵、条件熵、互信息的基本概念。

The basic concepts of uncertainty , information entropy , united entropy , term entropy and mutual information were introduced briefly .

针对这个问题，本文构建了一种基于边界域的变精度条件熵的度量标准。

Regarding this aspect , this paper builds a measure criterion on the variable precision conditional entropy of the scoped boundary .

其基本思想是利用信息论中熵与条件熵的度量来显示一个特征类型是否抓住了预测句法结构的主要信息。

The basic idea is that we use entropy and conditional entropy to measure whether a feature type grasps some of the information for syntactic structure prediction .

为估计利用各类成像传感器自然获取图像的无损压缩极限，提出一个利用多尺度条件熵和记忆性度量的实用方法。

Lossless compression bounds of images naturally acquired by various imaging sensors are estimated using a practical method based on multi scale conditional entropy and memory measurement .

应用负值量子条件熵，研究了在大失谐情况下，两全同二能级原子同时与一真空腔场相互作用任意态纠缠的时间演化。

It 's investigated that the entanglement of an arbitrary state between the two identical two-level atoms simultaneously interacting with vacuum cavity field by using negative quantum condition entropy .

给出了使用条件熵计算各个子分类器权值的方法。详细论述了算法的有效性和正确性。

In this paper , I propose a method to compute the conditional entropy for each sub dataset , and discuss in detail the validity and correctness of the algorithm .

本文利用条件熵和互信息的概念来衡量不同缺失属性之间对于类别贡献的差异，进而计算出各个子分类器的权重，使得最终的加权投票更加公平，结果更加准确。

In this paper , information entropy and mutual information are used to measure the difference between different attribute , and then the weight of each sub classifier is calculated .

将条件熵作为判断单一模型对结果贡献的依据，运用模糊综合评判确定对应的模型权重。

The conditional entropy is the basis to determine the contribution of a single model for the forecast result . Fuzzy comprehensive evaluation is used to determine the corresponding model weights .

隐性知识学习系统的目标是最小化隐性知识条件熵，最大化学习主体间的互隐性知识量。

The goal of the tacit knowledge learning system is to minimize the conditions entropy of the tacit knowledge , maximize the quantity of interactive of tacit knowledge between learning intersubjective .

结果表明，负值条件熵能够作为该条件下两全同二能级原子同时与单模腔场相互作用任意态的，容易解析计算的纠缠度。

The results show that the negative quantum conditional entropy can be regarded as a entanglement measure of an arbitrary states of the two identical two-level atoms with simple calculated analytically .

接着，详细介绍了本文提出的三种新的关键帧提取技术，分别为：基于自适应阈值聚类的方法、基于协方差的方法以及基于条件熵的方法。

After that , three new algorithms for key frame extraction are presented , which are based on clustering algorithm with adaptive threshold , based on covariance and based on conditional entropy .

本文定义了四种条件熵，并在此基础上提出了四种基于熵的方法，以用于粗糙集数据分析中的属性简约。

Four kinds of condition entropy are defined in this paper . Accordingly , four kinds of entropy based methods for the attribute reduction in the rough set data analysis are proposed .