隱含的決策 的英文怎麼說

中文拼音 [yǐnhándejuécè]
隱含的決策 英文
hidden decisions
  • 隱 : Ⅰ動詞（隱瞞; 隱藏） hide; conceal Ⅱ形容詞1 （隱藏不露） hidden from view; concealed 2 （潛伏的; ...
  • 含 : 動詞1 （東西放在嘴裏 不咽下也不吐出） keep in the mouth 2 （藏在裏面; 包含） contain 3 （帶有某種...
  • 的 : 4次方是 The fourth power of 2 is direction
  • 決 : Ⅰ動詞1 （作出主張; 決定） decide; determine 2 （執行死刑; 殺死） execute a person 3 （裂開; 斷開...
  • 策 : Ⅰ名詞1 （通「冊」 古代寫字用的竹片或木片） bamboo or wooden slips used for writing on in ancient ...
  • 隱含 : implication
  1. Because these datu are too large and discrete, it is a difficult and complex task to analysis categorize, index and look up them with traditional artificial methods. and it is more difficult to research into the implicit laws which hide in quality control and management. so, to explore the enterprise ' s information resources efficiently is impossible, and the quality of production can not be pledged well, not to mention make decision - making scientifically

    眾所周知，在水泥生產製造過程中，會產生大量的質量數據，但由於這些數據類繁、量大且分散，以至傳統的人工處理方式對其進行分析、歸類、檢索和查找成為一項艱巨復雜的工作，更難于揭示歷史數據中所隱含的質量控制和管理規律，從而難以對企業質量信息資源進行充分開發利用，致使企業產品質量難以提高，嚴重地影響質量決策的科學性。
  2. With dramatic advance and wide application of the internet and information systems, we can easily attain large quantities of data that is also in rapid increment daily. thereby it becomes impractical to handle these data manually. we wish that computers can automatically process these data and extract potentially useful knowledge from them to help us arrange managements and make decisions

    隨著網際網路和信息系統的迅猛發展和廣泛應用，人們可以輕易地獲得海量的數據，並且這些數據還在日益不斷地增長中，對這樣的數據仍然採用傳統的人工處理方法變得不切實際，於是希望計算機能夠自動地幫助我們處理這些海量的數據，並提取出隱含的有價值的知識，輔助管理和決策，這就需要研究者對機器學習，特別是數據庫知識發現作更加深入和廣泛的研究。
  3. The attitude of theorists and decision - makers to m _ 2 / gdp has transformed from the affirmation of the fast marketability and the monetization performance in 1990 ’ s into the concern regarding the reason of the growth of the ratio which involved the system problems and the high financial risk in china ’ s economy. the economist has produced own explanation from each kind of angle

    理論界與決策部門對m _ 2 / gdp數值變化的態度，已經從二十世紀九十年代前對快速市場化與貨幣化表現的肯定，轉變為當前對這一指標過高所隱含的體制問題與金融風險的高度關注對于m _ 2 / gdp的增長原因，經濟學家從各種角度給出了自己的解釋。
  4. The paper introduces some common methods used in data - mining ’ s calculating, process and data pretreatment. it digs out the implied connection among criminals ’ data information after analyzing and dealing with all kinds of criminals ’ data information in a certain prison ’ s database. and though the study on decision tree c4. 5 algorithm, we utilize the algorithm set up a model of criminal shortening the term of imprisonment, and give some classify of factor of criminal shortening the term of imprisonment

    本文介紹了數據挖掘常用的演算法，數據挖掘的步驟以及數據預處理技術，針對某一監獄管理局的罪犯信息數據庫，對罪犯數據間的各種信息進行分析處理，挖掘出了罪犯數據信息間的一些隱含關系，以及通過對決策樹c4 . 5演算法的研究，利用該演算法建立了罪犯減刑效果模型，用來對影響罪犯減刑效果的因素進行分類。
  5. In the paper, the relative concepts such as association rule and principles are analysed and elaborated systematically, systemanalysis and design is achieved by using well - defined and great function uml and datamanaging of sytem is realized through vb6. 0 to put the emplying knowledge out of the quantum datas, the paper also performs multidimension databases data forms on the support dicision layer

    論文中系統地分析並闡述了模型及關聯規則等相關概念及原理，採用定義良好、功能強大的uml （ unifiedmodelinglanguage ，統一建模語言）進行系統分析和設計，並用visualbasic6 . 0實現了系統的數據管理，為了更好地利用模型技術和數據挖掘抽取大量數據中的隱含知識，本文對支持決策的數據實行了多維數據庫數據組織形式。
  6. Data mining, also named as kdd ( knowledge discovery in database ), is a decision support method in which we can pick up many connotative, unkn - own, potential and useful knowledge or information mode from database or data warehouse. the knowledge that is discovered by means of data minning can reflect a certain facts and guide practice

    數據挖掘（ datamining ） ，又稱知識發現，是利用各種分析工具在存放海量數據的數據庫或數據倉庫中提取隱含的、先前未知的、潛在有用的知識或信息模式的決策支持方法。通過數據挖掘發現的知識能夠反映一定的客觀事實，並指導實踐。
  7. Data mining is a collection of engineering methods proposed for solving large practical problems of machine learning, pattern recognition, database technology etc. the purpose of data mining is to discover the implicit knowledge or rule, and to help human expert to make decision. frequent pattern mining ( fpm ) and bayesian network learning ( bnl ) are two useful technologies of data mining

    數據挖掘技術是解決機器學習、模式識別、數據庫技術等各種領域中的大型實際應用問題而提出的一些工程性方法的集合，主要是為了從大型數據庫中高效地發現隱含在其中的知識或規律，並為人類專家的決策提供支持。
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