bayesian classifier中文是什么意思

發(fā)音:

中文翻譯
造句

貝葉斯分類器

classifier n. 1.分類者。 2.【礦物】分級機(jī)。 3.【化學(xué)】 ...
bayesian 貝葉斯的; 貝葉斯定理的
classifier n. 1.分類者。 2.【礦物】分級機(jī)。 3.【化學(xué)】分粒器。 4.(漢語等中的)量詞。
bayes bayesian 貝葉斯定理的
bayesian algorithm 貝葉斯算法
bayesian analysis 貝葉斯分析
bayesian approach bayes氏方法; 貝斯分析法
bayesian classification 貝葉斯分類器
bayesian estimate 貝葉斯估計(jì)
bayesian estimation 貝葉斯估計(jì)
bayesian formula 式方程式
bayesian inference 貝葉斯定理; 貝葉斯推斷; 貝葉斯推理; 貝葉斯推論
bayesian learning 貝式學(xué)習(xí)
bayesian logic 貝葉斯邏輯
bayesian models 貝式模型法
bayesian network 貝式網(wǎng)路; 貝式網(wǎng)絡(luò); 貝葉斯網(wǎng)絡(luò)
bayesian networks 貝葉斯網(wǎng)絡(luò)
bayesian probability 貝葉斯概率
bayesian regression 貝葉斯回歸
bayesian statistic 貝葉斯統(tǒng)計(jì)
bayesian statistics 貝式統(tǒng)計(jì)學(xué); 貝葉斯統(tǒng)計(jì):一種尚有某些爭論的統(tǒng)計(jì)方法學(xué)
bayesian theorem 貝葉斯定理
bayesian theory 貝葉斯理論
bayesian decision method 貝葉斯決策方法
bayesian decision model 培欣決策模式

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例句與用法

Design of an improved spam filter based on naive bayesian classifier
垃圾郵件過濾器的改進(jìn)
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(xué)習(xí)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(gè)方面對貝葉斯分類模型進(jìn)行了深入的研究：半樸素貝葉斯分類與增量貝葉斯分類。
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(xiàng)式計(jì)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(gè)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(shí)例的選擇策略，本文研究的重點(diǎn)是如何充分利用訓(xùn)練集的先驗(yàn)知識并使其在學(xué)習(xí)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(jìn)行了總結(jié)，可作為進(jìn)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(yàn)概率計(jì)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(xué)習(xí)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(xué)習(xí)算法( cmi - bsnbc ) 。

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