Research and Application of Ensemble Learning Based on Conditional Mutual Information 基于条件互信息的集成学习的研究与应用
A class graphical models, called linear conditional mutual information graph, is proposed for identification structural vector autoregression model. 非线性自回归模型在结构向量自回归(VAR)模型辨识的图模型中引入信息论方法。
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. 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。
This paper, first of all, introduces a new α order mutual information, whereby α order conditional mutual information and α order entropy are defined. As a result, many interesting properties parallel to Shannon Information Measure are obtained. 本文首先引入了一种新的α阶互息,并由此定义了α阶条件互息及α阶熵,得到了与Shannon信息量平行的许多有趣性质。
Conditional Entropy and Mutual Information in Random Cascading Processes 自相似随机级联过程中的条件熵和信息量
In this paper, we proposed a new feature selection method called Conditional Mutual Information Maximin ( CMIM). It can select a set of individually discriminating and weakly dependent features. 提出了一种新的用于文本分类的特征选择算法(CMIM),它可以帮助选出区分能力强、弱相关的特征。
The automatic pinyin tagging strategies include: conditional probability strategy, mutual information strategy and rule strategy. 所采用的多音字自动注音策略有以下三种:条件概率策略、互信息策略以及规则策略。
Identification of Structure VAR Models Using Conditional Mutual Information Graphs 结构VAR模型辨识的条件互信息图模型
In the proposed method, the causal strength or cor-relation between two variables can be accurately quantified by tuning conditional mutual information. 该方法通过微调条件互信息来准确定量变量之间的因果强度或相关性强度,对于调控关系给出了一个简洁的计算模型。
Since most algorithms are not effective and not very meaningful in combining, this thesis proposes an algorithm based on a kind of Semi-Naive Bayesian Classifier which is measured by conditional mutual information ( CMI-BSNBC). 针对已有的学习算法中存在的效率不高及部分组合意义不大的问题,本文提出了条件互信息度量半朴素贝叶斯分类学习算法(CMI-BSNBC)。