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bayesian classifier in Chinese

Pronunciation:
How to pronounce "bayesian classifier""bayesian classifier" in a sentence

Translationmobile phoneMobile

  • 貝葉斯分類器

Examples

  • 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(cè)
  • 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(duì)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(cè)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(shí)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(duì)貝葉斯分類器,子空間模式識(shí)別和人工神經(jīng)網(wǎng)絡(luò)在字符識(shí)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(duì)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(duì)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 ) 。
  • More examples:  1  2

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