conditional entropy 中文意思是什麼

conditional entropy 解釋
帶條件的平均信息量
  • conditional : adj 1 帶有條件的，有限制的；視…而定的。2 【語法】條件的，假設的。3 引起條件反射的。n 【語法】條件...
  • entropy : n. 1. 【物理學】熵。2. 【無線電】平均信息量。
  1. Classification algorithm for self - learning naive bayes based on conditional information entropy

    基於條件信息熵的自主式樸素貝葉斯分類演算法
  2. An attribute reduction algorithm based on conditional entropy

    一種基於條件熵的粗糙集屬性約簡演算法
  3. Rules extraction method of decision tree based on new conditional entropy

    基於新的條件熵的決策樹規則提取方法
  4. And weighted infromation entropy and weighted conditional entropy of information system based on set - value function is presented also

    還提出了基於一般的集值函數的信息系統加權信息熵和條件熵概念。
  5. Discuss the generation of test strategy for diagnosis based on information gain maximization and conditional entropy minimization. this approach can be used into the testability analysis and the optimization of test strategy for diagnosis

    研究了基於信息增益最大和基於條件熵最小的診斷測試策略生成問題，該方法可用於系統的可測性分析、診斷測試策略的優化等方面。
  6. 3. discussing application of entropy in multivariate control chart, firstly, testing whether the correlation structure has changed by means of the mutual information approach. secondly, using approximately approach to discuss two estimators of conditional entropy. finally, establishing corresponding entropy chart

    討論了熵在多變量控制圖中的應用，首先利用相互信息量的方法檢測相關陣是否變化，然後利用近似的方法討論了兩種條件熵的估計量，建立了相應的熵圖。
  7. Through detailed analysis to information entropy concept of complete information systems, weighted information entropy and weighted conditional entropy of incomplete information system based on limited tolerance relation are studied, which is naturally genealization of rough entropy conept of complete information systems based on equivalence relations

    在仔細分析完備信息系統的信息熵概念的本質之後，通過引入權數，研究了基於限制容差關系的不完備信息系統加權信息熵和條件熵問題。
  8. It ' s a pity that although there are many papers and articles focused on data mining published every year, most of them deal with data mining concept and abstract algorithm theory, it is hardly to see their real implementation and application, in this context, when i was in my graduate exercitation in a company in beijing, which focus on developing supermarket software, i joined and completed an olap ( online analytical processing ) project, merchandise analysis and sale report system, which based on microsoft analysis service and microsoft sql server. i also design and implement three important algorithms : merchandise association rule algorithm based on multi - level merchandise category, supermarket member customer shopping frequent sequence generating algorithm, customer classification ( decision tree ) algorithm which based on information entropy and conditional probability tree, and they all achieve expected result

    本文作者在實習期間，參與並完成了基於微軟分析服務器的銷售分析與報表系統；並在公司即將開始的數據挖掘項目中，完成了多個重要演算法的設計和c + +程序實現：基於多層分類商品樹的商品關聯規則演算法，會員顧客的購物頻繁序列模式產生演算法；基於信息熵理論和條件概率樹的會員顧客分類（決策樹）演算法，並分別使用數據進行了測試，取得了較好的結果。
  9. 2 ) by analyzing the information and conditional information description mechanism of system states, the problem of stochastic model reduction is investigated based on state aggregation. the information loss and conditional information loss between the full - and reduced - order models are measured by entropy, while the independence and conditional independence within me components of aggregated state are measured by kullback - leibler information distance. several model reduction methods for stable and unstable linear systems are derived by employing two criteria to get aggregation matrices : the minimal information loss and the maximal independence

    2 ）分析了隨機系統狀態空間模型中的信息和條件信息描述機制，以shannon熵為手段描述線性系統模型降階過程中的信息和條件信息損失，以kullback - leibler信息作為衡量降階模型狀態向量各分量之間統計獨立性的測度，針對穩定和不穩定系統研究基於狀態集聚的模型降階問題：分別運用最小信息損失準則和最大獨立性原則，得出幾種狀態集聚的信息論方法，並討論降階模型的性質、階次的確定、系統噪聲分佈特性等問題。
  10. One attribute selection and reduction method is presented based on that using factor analysis technology to divide conditional attributes into groups to outline that conditional attributes in one attribute group is relevant to corresponding factor, and those factors are linear combination of target concept. information entropy evaluation is used for attribute selection based on that whether the attributes groups and attributes are strong correlation with corresponding target concept and factor, reserving attributes that are correlative to target concept, and deleting irrelevant attributes

    提出一種屬性選擇和屬性消減方法，引入因子分析技術對條件屬性進行分組，每個屬性類內部的條件屬性與相應的因子線性相關，所有因子是目標概念的線性組合，根據屬性類或屬性是否與相對應的目標概念或因子強相關，引入信息熵評價方式對之進行選擇，選擇出與目標概念相關的屬性，剔除無關的屬性。
分享友人
分享到 Line

分享到臉書