貝葉屬 的英文怎麼說
中文拼音 [bèiyèzhǔ]
貝葉屬
英文
conchophyllum-
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。Data generalization is a kind of data model in knowledge mining. fuzzy entropy and fuzzy modifying bayesian method are used to generalize the trouble diagnosis data in fms
採用基於模糊熵的最大增益和模糊修正貝葉斯的類屬演算法，計算fms的故障分類問題，說明了知識挖掘的數學過程。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。Bayesian classification model based on attribute correlation analysis
基於屬性相關性分析的貝葉斯分類模型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。( 3 ) the inhere drawback of traditional algorithms to construct bayesian networks is pointed out. so the local bayesian network metric attribute based - on bayesian dirichlet metric is also researched, and some notions on bayesian networks are presented, such as comparability, independency and symmetry on nodes etc. it is showed that the comparability is an important factor on whether two nodes is linked, and a sufficient condition for nodes with most comparability is obtained. by using this analysis on local bayesian network, the important principia are presented to create graph models
（ 3 ）由於構造貝葉斯網路圖的傳統演算法存在固有缺陷，本文通過探索貝葉斯網路圖的內在規律，主要研究基於bd度量的局部貝葉斯網路圖的度量屬性，提出網路圖中節點的相似性、獨立性及對稱性等新概念，得出相似性是兩節點連接與否非常重要的因素，對稱節點具有相同的bd度量值，獲得具有最強獨立性節點的充分條件。A further study has been made about decision tree classification, bayesian network, and discretization of conntinuous attributes, at the same time many kinds of classfication algorithms have been achieved
對決策樹分類、貝葉斯網路和連續屬性的離散化問題進行了的研究，實現了多種分類演算法。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。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。分享友人