貝葉斯分類 的英文怎麼說

中文拼音 [bèiyèsīfēnlèi]
貝葉斯分類 英文
bayes classification
  • 貝 : 名詞1 [動物學] （蛤螺等有殼軟體動物的統稱） cowry; cowrie; shellfis 2 （古代用貝殼做的貨幣） cowr...
  • 斯 : Ⅰ名詞（古代驅疫時用的面具） an ancient maskⅡ形容詞[書面語] （醜陋） ugly
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 貝葉 : （印度貝多羅 pattra 樹的葉子, 古代印度人用以寫佛經） pattra leaves
  1. 8th joint symp. computational linguistics, nanjing, 2005, pp. 217 - 220. 54 wang j b, du c l, wang k z. study of automatic abstraction system based on natural language understanding

    國內研究者探索了基於實例無指導學習方法互信息計算詞匯向量空間基於依存分析與貝葉斯分類模型結合等各種方法。
  2. An improved naive bayesian categorization algorithm for html

    文檔的樸素貝葉斯分類演算法
  3. Author develop three text dassiliers lilie naive - bayes classifier, k nearest neighbor classifier and svm classifier. furthermore, including the three classifiers, one text categorisation system is built up, and it has high prachcability

    作者採用三個模型，實現了樸素貝葉斯分類器、 k近鄰分類器和支持向量機分類器三個中文文本分類器，集成了一個實用性較強的實驗系統。
  4. Monte carlo is a method that approximately solves mathematic or physical problems by statistical sampling theory. when comes to bayesian classification, it firstly gets the conditional probability distribution of the unlabelled classes based on the known prior probability. then, it uses some kind of sampler to get the stochastic data that satisfy the distribution as noted just before one by one

    蒙特卡羅是一種採用統計抽樣理論近似求解數學或物理問題的方法，它在用於解決貝葉斯分類時，首先根據已知的先驗概率獲得各個類標號未知類的條件概率分佈，然後利用某種抽樣器，分別得到滿足這些條件分佈的隨機數據，最後統計這些隨機數據，就可以得到各個類標號未知類的后驗概率分佈。
  5. Mixed naive bayes classifier model

    混合式樸素貝葉斯分類模型
  6. 3 ) we construct the privacy preserving naive bayesian classifier

    3 ）構造了保持隱私的樸素貝葉斯分類器。
  7. Research on the method of processing empty value based on generalized naive bayes classifiers

    基於廣義樸素貝葉斯分類器的空值處理方法
  8. 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
  9. The key of model learning of semi - naive bayesian classifier is how to combine feature attributes effectively

    目前半樸素貝葉斯分類模型學習的關鍵是如何有效組合特徵屬性。
  10. Underlain by these models and theories, a crawling algorithm based on " tunneling " and bayes classification is explored and a mathematical function model of tunneling distance for this algorithm is established, and moreover the practical procedure of this crawling algorithm is discussed

    在這些研究的基礎上，本文探索了一種基於隧道效應和貝葉斯分類途徑的特定主題爬取演算法，並建立了這種爬取演算法的穿越距離函數模型，同時分析了這種演算法在爬取中的具體實現過程。
  11. Because of ineffectiveness of naive bayes model for text classification, this thesis proposed integrating boosting theory of machine learning in classification process, boost naive bayes categorization model through many times training. improved by experiments, mutual information and naive bayes integrated with boosting bring very good precision, recall, and f1 score

    鑒于樸素貝葉斯的分類效果不佳，本論文又提出將機器學習中的boosting思想結合到樸素貝葉斯的分類模型中，對樸素貝葉斯模型進行提升，實驗證明，改進的互信息和給合了boosting思想的樸素貝葉斯分類模型均產生良好的分類效果?分準率、分全率及f1值。
  12. This paper mainly deals with the multivariate bayesian inference theory used in the modern economical and management science. this includes the bayesian inference theory about three important kinds of linear models, including the single equation model, multiple equation model system and var ( p ) predictive model, and their application in economic forecasting and quality control, and also the design for the bayesian classification identification method among multiple populations

    本文主要研究現代經濟管理中的多元貝葉斯推斷理論，包括單方程模型、多方程模型系統和向量自回歸var （ p ）模型的貝葉斯推斷理論及其在經濟預測與質量控制中的應用，以及多總體的貝葉斯分類識別方法的構造。
  13. Unlike other classifications, bayesian classification bases on mathematics and statistics, and its foundation is bayesian theory, which answers the posterior probability. theoretically speaking, it would be the best solution when its limitation is satisfied

    與其它分類方法不同，貝葉斯分類建立在堅實的數理統計知識基礎之上，基於求解后驗概率的貝葉斯定理，理論上講它在滿足其限定條件下是最優的。
  14. The oblivious polynomial evaluation protocol will be used many times in our privacy preserving naive bayesian classifier, so its efficiency is important to the solution

    健忘多項式計算協議在保持隱私的樸素貝葉斯分類器協議中多次用到，因此協議的效率是一個需要關心的問題。
  15. Theoretical analyses and experimental results demonstrate that this method is very effective. also, bayesian classifier, subspace method and ann are summarized in this chapter. they can be used for the next research

    本章還對貝葉斯分類器，子空間模式識別和人工神經網路在字元識別中的應用進行了總結，可作為進一步研究的基礎。
  16. By constructing two secure posterior probability evaluation protocols to deal with discrete and numeric, or categorical and continuous attributes respectively, we attain the naive bayesian classifier without preamble

    本文針對離散值屬性情形和連續值屬性情形分別構造了保持隱私的后驗概率計算協議，最後獲得安全的樸素貝葉斯分類器協議。
  17. Specifically, aiming at two widely used algorithms in data mining, naive bayesian classifier and boolean association apriori algorithm. we have brought forward two corresponding protocols incorporating privacy concerns. we have used secure multi - party computation protocols and tools to get the solutions

    本文針對數據挖掘中應用較為廣泛的樸素貝葉斯分類器和關聯規則的apriori演算法，利用安全多方計算的理論和工具，給出了與其相應的隱私性演算法。
  18. Along the medial axes with the same steps, we can get the average integral gray profile and the width profile. 3. karyotype classification of chromosome : according to the chromosome ’ s geometrical parameters, the karyotype classification are realized through the normal bayes classifier or fuzzy clustering based on the character of average integral gray profile

    利用染色體的中線等步長提取平均積分輪廓和寬度輪廓; 3 、染色體的核型分類:利用染色體結構參數即其灰度輪廓通過正態貝葉斯分類器或採用模糊聚類的方法來實現染色體的對號分類。
  19. Classification has always been a central issue on data mining, machine learning and pattern recognition, classifier, as an important model and method of machine learning and data mining, is very important to the development and application of machine learning and the data mining. the classifier ’ s effect closely correlates with the characteristic of data sets, at present, the construction of classifier is generally based on the character of different datasets, there is no such a classifier which is suitable for any data sets. under uncertain conditions, the bayes network is a powerful tools for the knowledge expression and inference, but for difficulties in constructing its network structure and very high time complexity, it has not been considered as a classifier algorithm until the emergence of na ? ve - bayes classifier

    分類一直是數據挖掘、機器學習和模式識別等研究的核心問題，貝葉斯網路是作為知識表示和推理的強大工具，由於搜索空間巨大和學習困難的原因，直到樸素貝葉斯理論的出現才被作為分類器演算法，改進樸素貝葉斯分類器是貝葉斯分類器學習的一個主要的研究方向。遺傳演算法本質上是一種求解問題的高效并行全局搜索演算法，適合應用於那些改進的分類器的結構學習中。本文提出了一種基於遺傳演算法的ban分類器演算法。
  20. Principle and algorithm of incremental bayes classification

    增量式貝葉斯分類的原理和演算法
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