bayesian classifier造句
例句與造句
- Design of an improved spam filter based on naive bayesian classifier
垃圾郵件過濾器的改進 - Face detection with bayesian classifier
基于貝葉斯判別器的面部檢測 - 3 ) we construct the privacy preserving naive bayesian classifier
3 )構(gòu)造了保持隱私的樸素貝葉斯分類器。 - 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(guān)鍵是如何有效組合特征屬性。 - This thesis makes a study of two bayesian classifying models which are semi - naive bayesian classifier and increasing bayesian classifier
本文從兩個方面對貝葉斯分類模型進行了深入的研究：半樸素貝葉斯分類與增量貝葉斯分類。 - It's difficult to find bayesian classifier in a sentence. 用bayesian classifier造句挺難的
- 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(xié)議在保持隱私的樸素貝葉斯分類器協(xié)議中多次用到，因此協(xié)議的效率是一個需要關(guān)心的問題。 - The key of increasing bayesian classifier is the policy of how to choose test samples . this thesis studies how to make full use of prior knowledge and transmit it
增量貝葉斯分類模型的關(guān)鍵是測試實例的選擇策略，本文研究的重點是如何充分利用訓練集的先驗知識并使其在學習過程中向前傳遞，提出了新的模型。 - 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(jīng)網(wǎng)絡(luò)在字符識別中的應(yīng)用進行了總結(jié)，可作為進一步研究的基礎(chǔ)。 - 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(xù)值屬性情形分別構(gòu)造了保持隱私的后驗概率計算協(xié)議，最后獲得安全的樸素貝葉斯分類器協(xié)議。 - 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 )
針對已有的學習算法中存在的效率不高及部分組合意義不大的問題，本文提出了條件互信息度量半樸素貝葉斯分類學習算法( cmi - bsnbc ) 。 - 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(jié)構(gòu)進行了擴展，其目的是為了突破樸素貝葉斯分類模型特征屬性間獨立性假設(shè)限制，提高分類性能。 - Bayesian classification is based on bayesian theorem . it can be comparable in interpretability with decision tree and in speed with neural network classifiers . bayesian classifiers have also exhibited high accuracy and speed when applied to large databases
該算法基于貝葉斯定理，可解釋性方面可以與判定樹相比，準確度可和神經(jīng)網(wǎng)絡(luò)分類算法相媲美，用于大型數(shù)據(jù)庫時該算法已表現(xiàn)出高準確度與高速度。 - 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(shù)據(jù)挖掘中應(yīng)用較為廣泛的樸素貝葉斯分類器和關(guān)聯(lián)規(guī)則的apriori算法，利用安全多方計算的理論和工具，給出了與其相應(yīng)的隱私性算法。