tree building method 中文意思是什麼

tree building method 解釋
建樹法（網路分析法應用例）
  • tree : n 特里〈姓氏〉。n 1 樹〈主要指喬木，也可指較大的灌木〉。 ★玫瑰可以稱為 bush 也可以稱為 tree 2 木...
  • building : n. 1. 建築物，房屋，大樓，大廈。2. 製造；營造，建築；組合，組裝；建築術。
  • method : n 1 方法，方式；順序。2 （思想、言談上的）條理，規律，秩序。3 【生物學】分類法。4 〈M 〉【戲劇】...
  1. Aligned sequences were imported into mega2 and paup * 4. 0 for phylogenetic analysis using mp, ml, me and nj method. in each tree - building method, several distance measures or algorithms were used, but all these distance measures or algorithms produced essentially the same results as follows : 1

    使用四種常用的建樹方法： nj法、 me法、 mp法以及ml法，對斑翅蝗科部分昆蟲的系統進化關系進行研究，幾種方法所產生的分子樹的拓撲結構大致相同，僅在個別種的歸屬上存在差異。
  2. In the course of building knowledge base, this article analyses field knowledge of the automobile components failure with fault tree analysis ( fta ), and represent the field knowledge included in fault tree with the combination of frame and production rule on the base of studying the theory and method about knowledge representation

    在知識庫的建造過程中，本文應用故障樹分析法分析了汽車零部件失效領域知識，在討論了知識表示的有關原理和方法的基礎上，採用了產生式系統和框架系統相結合的方法表示故障樹中蘊涵的領域知識。
  3. Firstly, some basic algorithms for inducing decision tree are discussed, including id3, which uses information gain to select a splitting attribute when partitioning a training set ; c4. 5, which can deal with numeric attributes ; cart, which uses gini rule in attribute selection and induces a binary tree ; public, which puts tree pruning in the tree building phase ; interactive method, which puts artificial intelligence and human - computer interaction into the procedure of decision tree induction ; as well as sliq and sprint which are scalable and can be easily parallelized. advantages and disadvantages of these algorithms are also presented

    文中詳細闡述了幾種極具代表性的決策樹演算法：包括使用信息熵原理分割樣本集的id3演算法；可以處理連續屬性和屬性值空缺樣本的c4 . 5演算法；依據gini系數尋找最佳分割並生成二叉決策樹的cart演算法；將樹剪枝融入到建樹過程中的public演算法；在決策樹生成過程中加入人工智慧和人為干預的基於人機交互的決策樹生成方法；以及突破主存容量限制，具有良好的伸縮性和并行性的sliq和sprint演算法。
  4. This paper organizes as follows : chapter 1 mainly introduces the research background, and gives a description of the research content of this paper after discusses the research trend on classification in dataming ; chapter 2 focuses on the theory of cart and gives a algorithm which integrates the tree building phase and pruning phase ; chapter 3 gives a detailed description on rbf and proposes the center selection method based on the statistic information of the training dataset. chapter 4 is the design and realization of the classifier. and chapter 5 summarize the work we i done and analyses the work to do later and obtain the research subject in the future

    本論文的組織結構為：第一章為緒論，介紹了研究背景和分類分析研究領域國內外的研究動態，闡述了本文的主要研究內容；第二章詳細敘述了cart分類器的原理，給出了建樹與修剪階段合併的演算法；第三章描述了rbf神經網路分類器，給出了基於樣本統計的網路中心選取方法；第四章講述了cart分類器的設計與實現；第五章對本課題的研究工作進行了總結，分析了今後所要進行的工作以及進一步研究的課題。
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