順序分類演算法 的英文怎麼說

中文拼音 [shùnxùfēnlèiyǎnsuànfǎ]
順序分類演算法 英文
sequential classification algorithm
  • 順 : Ⅰ介詞1 （向著同一個方向） in the same direction as; with 2 （依著自然情勢; 沿著） along; in the d...
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 演 : 動詞1 （演變; 演化） develop; evolve 2 （發揮） deduce; elaborate 3 （依照程式練習或計算） drill;...
  • 算 : Ⅰ動詞1 （計算數目） calculate; reckon; compute; figure 2 （計算進去） include; count 3 （謀劃;計...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 順序 : 1. （次序） plain sequence; subsequence; order; sequence; succession2. （順著次序） in proper order; in turn
  1. In this paper, a lot of researches and exploration are applied to studying the universality and expansibility of hardware and the arithmetic design and code optimization of software. especially, all of the following arithmetics or conceptions are worked out in the research of software design : self - adaptable compression arithmetic based on dictionary model for data collection system, similarity full binary sort tree, a optimized quick search arithmetic and an improved arithmetic of multiplication in the floating - point operation. and all of the arithmetic are designed with mcs - 51 assembly language. the quick search arithmetic, in which merits of both binary search and sequence search are used fully, are based on the specialty of preorder traversal in similarity full binary sort tree

    特別在軟體設計研究中，提出了適用於數據採集系統的數據壓縮演算法? ?基於字典模型的自適應壓縮演算法；提出了類滿二叉排序樹的定義；提出了基於類滿二叉排序樹的先序遍歷特性的最優化快速查找演算法，它充分利用了折半查找和順序查找各自的優點；提出了浮點運算乘法的改進演算法；並在mcs - 51匯編語言層次上對所有的演算法加以實現。
  2. This method is also valid for identifying the circuit and branch defect of first group spatial linkages based on the equivalent of circuit properties of the first group spatial linkages and its equivalent sphere four - bar linkage base on the property of the solutions of quartic equation, the conclusion that the number and order of branch between two adjacent stationary positions of the input link are derived. then, the new method to identify circuits of spatial single - loop linkages with four closures is presented. all types of the manner on which the branches coalesce at the stationary positions of the input link are obtained and the procedures to determine the type automatically are developed

    基於一元四次方程解的性質，得到了在輸入構件兩個相鄰瞬時靜止位置之間機構的分支的數目和大小順序不變的結論，進而提出了識別具有四個封閉形的空間單環機構迴路的新方法一一死點法，綜合出了輸入構件位於瞬時靜止位置時機構分支結合情況的所有類型及其自動判別方法，研究了由所有結合的分支信息自動生成迴路的演算法，首次解決了此類機構迴路與迴路缺陷的自動識別。
  3. Microsoft sequence clustering algorithm to create a sequence clustering mining structure

    Microsoft順序分析和聚類分析演算法
  4. Traditional rule - based classifiers train rules by using sequential covering technique, but the technique can make the models cover many examples of non - target class ( negative examples ) and fail to classify rare class

    傳統的基於規則的分類演算法多是採用順序覆蓋技術訓練分類規則，訓練得到的模型覆蓋大量的非目標類實例，對稀有類分類時效果很差。
  5. This tutorial walks you through scenarios for targeted mailing, forecasting, market basket analysis, and sequence clustering, to demonstrate how to use the data mining algorithms, mining model viewers, and data mining tools that are included in microsoft sql server 2005 analysis services ssas

    本教程將指導您演練目標郵件、預測、購物籃分析以及順序分析和聚類分析等方案，闡釋如何使用microsoft sql server 2005 analysis services （ ssas ）提供的數據挖掘演算法、挖掘模型查看器以及數據挖掘工具。
  6. Traditionally, we used c - means method, clusters similar data instances together into clusters and distance metrics are used on clusters to determine what is an anomaly. but there is some disadvantages in this method, such as the results of the cluster is sensitive to the data input sequence, furthermore, it is a local optimum algorithm

    聚類分析是數據挖掘技術中的關鍵技術，但傳統的c ?均值聚類演算法對入侵檢測數據進行處理有很多不盡人意的地方，如該聚類演算法是局部尋優演算法，聚類的結果對數據輸入順序比較敏感等。
  7. The algorithm of sequencable mark and description of the object for crack automatic identification is presented by means of pre - image process. on basis of visual c + + 6. 0 developing environment, the software function of controlling of magnetic partical testing engine and the stepping - motor is realized in c + + and mfc with objected programming method. the automatic system of the camshaft of small diesel engines automatic magnetic partical testing is realized, which is the predicted goal that we would achieve

    用計算機控制磁粉探傷機和步進電機的工作；解決了jpeg圖象格式在windows系統中visualc + +編程環境下的壓縮轉換、顯示和處理的問題；結合數字圖象的預處理，提出了通過圖象分析自動識別裂紋的順序目標標記與描述演算法；基於visualc + + 6 . 0開發環境，用c + +語言和mfc類庫，採用面向對象的程序設計方法，用軟體實現了對磁粉探傷機和步進電機等硬體系統的自動控制功能；實現了柴油機凸輪軸熒光磁粉探傷系統的自動化，達到了預期的目標。
  8. Every class endues a binary code, then a set of svms are used to solve the multiple binary problems. the generalization performance of ecc - svm is analyzed, which is determined by code length, hamming distance, coding sequence and margins of svms

    本文提出了基於糾錯編碼的svm多類分類演算法（ ecc - svm ） ，並分析了ecc - svm的推廣能力與編碼長度、碼間漢明距離、編碼順序以及分類間隙等之間的關系，給出了這種關系的數學描述。
  9. The order of our discussions " about these tasks is as follows : firstly, we pay more attention to the characteristics and difficulties of its environment including the concept, typical system model, main challenges, mobile network connection and soft application. secondly, according to mobile specialties of the environment we make the sort of data into four kinds : general data, time series, spatial data and time - spatial data, and present general processing of data mining. lastly, we discuss the methods of data mining of these four kinds respectively : after the introduction of the actuality of data mining of every kind, an algorithm of rule updating based on rough set is given, then put forward the processing of data related to mobile users and flow chat according to characteristics of the other three kinds

    本文對以上任務的討論順序安排如下：首先是對移動計算環境的技術特點和難點進行討論，包括移動計算的概念和典型系統模型、主要挑戰、移動聯網以及軟體應用這幾個大的方面；其次根據移動環境的移動特性把移動計算環境中的數據分為普通數據，時間數據，空間數據以及時空數據，提出了在移動計算環境中數據挖掘的一般流程；接下來分別對這四類數據進行挖掘演算法的討論：每一部分都是先介紹該類數據的挖掘方法研究現狀，對于普通數據，針對我們已提出的一種挖掘演算法-粗糙集演算法（ rs ） ，提出了對應的規則更新演算法，對於後三種數據，本人根據其在移動計算環境中的特點分別提出了與移動用戶相關的該類數據的一種具體的處理方法和演算法流程圖，包括基於移位連接方法的多屬性時間序列的挖掘演算法，基於apriori演算法的空間關聯規則數據挖掘方法以及關于移動用戶移動模式的時空數據挖掘方法，並用matlab對其中的規則更新演算法和時間序列的挖掘演算法這兩方面進行了實例模擬。
  10. In this lesson, you will learn how to create a mining model that can be used as part of a sequence clustering scenario. you will also learn how to explore mining models that are built with the microsoft sequence clustering algorithm

    在本課程中，您將學習如何創建用於順序分析和聚類分析方案的挖掘模型，還將學習如何利用通過microsoft順序分析和聚類分析演算法生成的挖掘模型。
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