樸素貝葉斯 的英文怎麼說

中文拼音 [sùbèiyèsī]
樸素貝葉斯 英文
naive bayes
  • 樸 : 樸形容詞（樸實; 樸質） simple; plain
  • 素 : Ⅰ形容詞1 （本色; 白色） white 2 （顏色單純） plain; simple; quiet 3 （本來的; 原有的） native Ⅱ名...
  • 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
  • 樸素 : simple; plain
  • 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. Spam filtering gateway based on nb algorithm

    基於樸素貝葉斯演算法的垃圾郵件網關
  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. Tree augmented naive bayes, tanb

    提出了一種基於樹擴展樸素貝葉斯
  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. An improved digital watermarking algorithm based upon relationship in dct domain

    該方法基於傳統的樸素貝葉斯
  8. A high effective network intrusion detection system based on tree augmented na ve bayes

    基於樹擴展樸素貝葉斯的高效網路入侵檢測系統
  9. 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
  10. 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
  11. 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。
  12. 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。
  13. 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。
  14. 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。
  15. 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。
  16. 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。
  17. Semi - naive bayesian classifier extends the structure of naive bayesian classifier in order to get rid of the limit of the assumption of independence between feature attributes of naive bayesian classifier and improve the performance of classification

    半樸素貝葉斯分類模型對樸素貝葉斯分類模型的結構進行了擴展，其目的是為了突破樸素貝葉斯分類模型特徵屬性間獨立性假設限制，提高分類性能。
  18. The thesis analyses and parses openoffice. org document from text classification viewpoint, depicts the methods of extracting content, formatting, structure and descriptive information, which are most related to classification, from openoffice. org documents, and then constructs three different classification models for openoffice. org documents, respectively called label components classifier, structure components classifier and comprehensive classifier. the thesis implements these three classifiers through na ? ve bayes

    本文從面向分類的角度深入地分析了openoffice . org文檔，描述了從文檔中抽取與分類最相關的內容和格式、結構以及文檔描述信息的方法，構建了標簽組件法、結構組件法和綜合法三種不同的文本分類模型，最後用樸素貝葉斯方法實現了openoffcice . org文檔的三種分類器。
  19. This article has analyised the bayesian theory and proposed a way of improving its filtering technique against chinese mails. after pre - handling the mails. it will deal with them by phrases and then compress the characteristic dimension of the mail collection by using the reduction method of the best attribute of the dependent rough set

    對樸素貝葉斯理論作為中文郵件過濾技術進行了分析改進，郵件預處理后，對其進行分詞處理，利用基於依賴性的粗糙集最優屬性約簡方法來對郵件集進行特徵維數壓縮。
  20. Naive bayesian classification algorithm is not satisfying when deployed to continuous attribute. therefore, the paper proposes a new discretization method under the hint of holte ' s 1r ( one rule ) discretization technique and the mechanism of entropy

    樸素貝葉斯分類演算法應用於連續屬性值時並不太理想，為此本文結合holte的1r離散化方法和熵的原理，提出了一種新的離散化方法。
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