中英
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bayesian classifier中文是什么意思
- 中文翻譯
- 造句
- 貝葉斯分類器
- classifier n. 1.分類者。 2.【礦物】分級機。 3.【化學】 ...
- bayesian 貝葉斯的; 貝葉斯定理的
- classifier n. 1.分類者。 2.【礦物】分級機。 3.【化學】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
- bayesian estimation 貝葉斯估計
- 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 貝式學習
- 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; 貝葉斯網(wǎng)絡
- bayesian networks 貝葉斯網(wǎng)絡
- bayesian probability 貝葉斯概率
- bayesian regression 貝葉斯回歸
- bayesian statistic 貝葉斯統(tǒng)計
- bayesian statistics 貝式統(tǒng)計學; 貝葉斯統(tǒng)計:一種尚有某些爭論的統(tǒng)計方法學
- 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
- 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。 - 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)絡在字符識別中的應用進行了總結，可作為進一步研究的基礎。 - 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 ) 。
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