粗分類法 的英文怎麼說

中文拼音 [cūfēnlèifǎ]
粗分類法 英文
board classification
  • 粗 : Ⅰ形容詞1 （長條東西直徑大的） wide （in diameter）; thick 2 （長條東西兩長邊的距離寬的） wide （i...
  • 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...
  • 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...
  1. Rough set classification algorithm based on pl sql

    的粗糙集分類演算法研究
  2. Rough set theory is a data mining approach capable of dealing with incomplete and inaccurate data, and it has been successfully applied to artificial intelligence and knowledge discovery, pattern recognition and classification, and fault detection

    粗糙集理論是數據挖掘的方法之一，它是處理模糊和不確定知識的一種數學工具，已在人工智慧與知識發現，模式識別與分類，故障檢測等方面得到了較好應用。
  3. This paper proposed some methods for finding out sure regions and ambiguous regions defined by lower and upper approximations in rough set theory. an applicable ending - criterion for semi - supervised back - propagation algorithm was proposed and a new rough classifier framework was studied, the assessment results show the effectiveness of the proposed criterion. a new classifier based on support vector machines was studied and applied

    本文提出了幾種劃分樣本邊界區的方法:提出了一種應用於半監督bp演算法的實用結束判據，並根據粗糙集理論，研究了一種新的粗糙分類機制，取得良好的效果；應用支持向量機理論，構造分類器並劃分樣本邊界區；最後研究多個分類器集成的方法尋找樣本邊界區，同樣提高了暫態穩定評估的可靠性
  4. Many different techniques have been proposed for classification, including statistical approaches, neural networks, decision tree algorithm and rough sets

    現有數據分類方法有統計方法、決策樹分類方法、神經網路方法、粗集法等。
  5. 11 mounlinier i, ganascia j g. applying an existing machine learning algorithm to text categorization. in connectionist statistical, and symbolic approaches to learning for natural language processing, wermter s, riloff e, scheler g eds., heidelberg, germany : springer verlag, lecture notes in computer science, vol

    由於挖掘出的特徵項目集可能很多，為了進一步的精簡項目集，提出了一個以可變精度粗糙集模型為基礎的方法對每個特徵頻繁項目集對分類的貢獻進行評估，剪除那些對最後的分類效果貢獻不大的項目集，並用精簡后的項目集構造每類文檔的主題模板。
  6. We can prove from the result of experiment that the web text mining approach could be more efficient than other classification algorithms whatever in precision, recall rate, or in novelty of knowledge. moreover, the technology is language independence

    從實驗結果看，基於粗糙集的web文本分類演算法無論在分類精度、分類效率，還是知識的新穎程度方面，都比以往分類演算法有明顯提高；而且，這種技術是語言獨立的。
  7. It turns out with practical examples that the classification error can be greatly reduced by virtue of rough set theory methodology

    結合實例說明了在聚類分析過程中，可以應用粗糙集方法有效地降低誤分類率。
  8. Using the statistic characterization of data, the relevant knowledge reduction algorithm is put forward by combining the probability with classification rules ; using the characterization of fuzzy attributes, the decision system with subjection degree attribute is built by combing the rough set theory and fuzzy set theory, and the idea of distinguish matrix is induced to the concealed decision system to reduce data

    利用數據的統計特徵，將概率測度與分類規則結合起來，提出了相應的知識西北工業大學博士學位論文約減演算法;利用模糊屬性集合的特點，把粗糙集合與模糊集合有機結合起來，將粗糙集中分辨矩陣的思想引入到具有隸屬度屬性的隱式決策系統中進行數據約減。
  9. Power system fault diagnosis can be described as a mode classified problem, and it is quite fit to apply the decision table method of rough sets theory

    電網故障診斷可以用一個模式分類問題來描述，很適合應用粗糙集（ rs ）理論的決策表方法。
  10. Conventional iec three ratio method and new iec ratio criterion are reduced according to the method of decision table in rough set theory respectively, simplified diagnosis rules are proposed. besides being identical with original iec method, the codes of conventional iec three ratio method are increased and bounds of new iec ratio criterion are augment by using this method, codes imperfectness of conventional iec three ratio method and code name absence of new iec ratio criterion are improved to some extent, scarcity of codes in conventional iec three ratio method is offseted, absolutization of sort and boundary in new iec ratio criterion is overcomed. this is important in practice for its flexibility and enhanced error tolerances

    應用粗糙集理論中的決策表化簡的方法分別對常規iec三比值診斷表和iec新導則診斷表進行了化簡，得到了簡化的診斷規則，它們不但具有與常規iec三比值法和iec新導則完全相同的診斷分類能力，而且擴充了常規iec三比值表的編碼范圍和iec新導則的診斷范圍，在一定程度上改善了常規iec三比值表編碼缺損和iec新導則無對應代號的問題，彌補了常規iec三比值法編碼的不足，克服了iec新導則分類及邊界的絕對化，使得iec診斷法在實際診斷中更具靈活性、實用性和容錯性，提高了故障診斷能力。
  11. There are two steps in the combined classifier. the least distance pattern recognition method is used to classify roughly in the first step

    組合分類器的結構如下：第一級分類器採用最小距離法進行粗分類。
  12. The thesis also examines the rough set model based on classification accuracy. the mie - rs data mining approach given later is based on the model

    另外，作者提出了基於分類正確度的粗糙集模型，該模型已用於作者研製的數據挖掘方法mie - rs上。
  13. The second phase it uses the ahp to select the right partner on considering the factors that influence the decision. the third phase it uses the dea to select partner from the candidate. the last phases it uses the portfolio and the multi - object theory to combine the partner

    第一階段利用粗選確定夥伴選擇的論域范圍；第二階段利用初選，採用層次分析法（ ahp ） ，綜合考慮影響決策的各類因素，篩選出合適的合作夥伴；第三階段精選，採用dea方法對候選夥伴進一步篩選；第四階段利用優化組合，用多目標理論對所選擇的合作夥伴進行最佳組合決策。
  14. First classifier chooses two classes whose matching distance between it and paper currency is bigger than others from all class. then in the second stage, we extract some new feature and improve the classifier to generate the last result. in the stage of defect detection for paper currency, we advances a homogeneity based algorithm for the detection of scratch and cracks appearing on paper currency, in which the homogeneity feature of the sensed paper currency image is first constructed to locate the pixels that probably been polluted, the image registration algorithm is subsequently used to overlay the sensed and reference paper currency image

    在特徵提取中，我們對基於方向塊的特徵提取方法進行了分析，在此基礎上針對美元特點，對圖像方向塊的劃分方式做了研究，並提出了基於幾何距離的特徵提取方法;在分類器設計中，我們採用了lvq網路對紙幣進行學習與分類，並提出了一種具有兩層結構的分類演算法，第一層首先對輸入的特徵向量進行粗分類，選定與特徵向量匹配距離最大的兩類幣種，進入第二層分類器;在第二層分類器中，我們通過研究進入該模塊兩類幣種特徵塊的相關性，重新設計了特徵向量，同時對分類器進行改進，最終實現對紙幣的分類。
  15. Second, a full automatic classification of raw images into textured and non - textured images is presented for large - scale image databases. the algorithm uses region segmentation and statistical testing

    其次針對圖像檢索數據量龐大的特點，按照由簡單到復雜的層次檢索的思想，提出了對圖像進行完全自動的粗分類演算法。
  16. The model made rough classification to the human ears first according to their geometric features, then ica was used to extract the algebra features and support vector machine ( svm ) was for detailed classification, finally the results were achieved, which was in accordance with human natural recognition process

    該模型首先根據人耳的幾何特徵對人耳進行粗分類；然後應用獨立分量分析的方法提取代數特徵，支持向量機進行細分類，最後給出分類結果。
  17. In this paper, first, the five modules in the system are explained in detail including the input of chinese character, preprocessing, rough classification, fine classification and post - processing. especially as to the neural network classifier, we not only discuss the fundamental principle of bp network, feature extraction, the realization of bp network, the selection of network structure and parameters, but also discuss its drawbacks and its improved solutions

    本文首先對系統中漢字輸入、預處理、粗分類、細分類和后處理五大模塊進行了較詳細的說明，特別是對神經網路分類器，不僅討論了其原理、特徵提取、 bp演算法實現和網路結構及參數選擇，還探討了bp演算法的缺陷問題並提出了改進方法。
  18. This paper designs a two - stage classifier, which is based on the combination of the covering approach, fuzzy set theory and the possible set based method. experimental results show that this classifier has a good performance

    本文嘗試將基於可能集合的粗分類方法與模糊球面領域覆蓋思想相結合，設計出一種兩級分類器，實驗表明這是一種效果較好的分類器。
  19. We adopt two different characteristic extracting methods and recognition algorithms accordingly. thirdly, we researched the neutral network algorithm and its improvement algorithm, designed a bp neutral network, which could apply in chinese handwritten recognition

    漢字識別的分類演算法包括對漢字進行粗分類和細分類，在不同的分類方法中各採用兩種互補的特徵抽取演算法，並相應地在識別上採用不同的策略。
  20. There are three steps in the combined. firstly, the template matching method is used to classify roughly in the first step ; secondly, the fuzzy pattern recognition method which are connected paralled is used to classify finely ; at last, the third classifier, which input is the output of the second classifiers, raise one ’ s hand as its decision rules

    第一級分類器採用模板匹配法進行粗分類;第二級分類器由并行的模糊模式識別分類器組成，進行細分類;第三級分類器是綜合分類器，它將第二級的輸出作為輸入，根據舉手錶決規則得到最後的輸出結果。
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