粗分類 的英文怎麼說
中文拼音 [cūfēnlèi]
粗分類
英文
lumping-
Knowledge and classifications are related together by the theory of rough sets which claim is that knowledge is deep - seated in the classificatory abilities of human beings
摘要粗糙集理論將知識與分類聯系在一起，認為知識就是人類所具有的分類能力。Classification model is based on typical rough set theory and entropy theory, select the attribute according to entropy theory, determine the equivalence classes according to indiscernible relation, then extract the classification rules
分類模型是以經典的粗糙集合理論和信息熵理論為基礎，依據信息熵理論對屬性進行篩選，依據不可區分關系確定等價類，從而提取決策規則。Rough set classification algorithm based on pl sql
的粗糙集分類演算法研究Road foundations. pozzolana and lime bound granular material. definition. composition. classification
路面層.粗砂.火山灰.石灰.定義.混合物.分類A real rough set space and the concepts of real lower and upper approximation corresponding to real - valued attributes is studied. a rhombus neighborhood for som is proposed, and the combination of som and rough sets theory is explored in the dissertation. according to the distance between the weight of winner node and the input vector in the real rough sets space, some new weights learning rules are defined
本文提出採用菱形鄰域代替一般的方形鄰域，可以減少待修正權重的數目;並利用實數粗糙空間的下、上近似集的精確概念劃分自組織映射的輸出結果，使得改進后的映射結果中各類樣本點之間有明顯的間隔，易於進行分類識別。Such a gross classification certainly does not take into account the many smaller ribosome size differences reported.
這樣一個粗略的分類自然沒有考慮到已有報道的大小不同的許多較小的核蛋白體。Application of a rough - set neural network to superfamily level in insect taxonomy
粗糙集神經網路在昆蟲總科階元分類學上的應用With the rough sets theory, people can process the undistinguished problem using uncompleted information or knowledge. and also enhance the capability to classify the imprecise data coming from observation or measurement
粗糙集理論反映了人們以不完全信息或知識去處理一些不可分辨現象的能力，或依據觀察、度量得到某些不精確的結果而進行數據分類的能力。It is pointed out that an applicable computer wi system is human - computer interactive, composed of rough classification by computer and final decision by human experts
指出實用的計算機筆跡鑒別系統是人機結合的、機器粗分類和人工專家最終判決的系統。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。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。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。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。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。According to the successful application to pattern recognition of small category for neural network, in this system, we use a distance classifier based on gross periphery feature for rough classification in order to classify the total chinese character set to some small sets, and then a bp network classifier based on the probability distribution of pixels with elastic meshing is used for fine recognition
在此系統中，我們針對神經網路在小類別模式識別中的成功應用，先採用基於漢字粗外圍特徵的距離分類器作為粗分類，以將待識漢字集分成若干個小的漢字集合，然後用基於漢字彈性網格像素概率分佈特徵的bp神經網路分類器作為細分類，以實現漢字識別的目的。In this paper, three key issues are studied in the analysis of basketball video : shot segmentation, shot coarse classification and detection and location of scoreboard
本文針對籃球視頻分析中的關鍵技術進行研究，主要包括鏡頭分割，鏡頭粗分類及記分牌檢測和定位。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。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。A algorithm of shot coarse classification is proposed, firstly, key frames are extracted in equal interval in a shot ; then for a frame, dominant colors are obtained by hue, furthermore, the variance of dominant colors and ratio of clustered colors are computed, by which the corresponding frame could be classified into playing or non - playing frame, finally, a shot could be classified by the percentage of playing frames
2 .在鏡頭粗分類中，首先在每個鏡頭內等間隔地提取關鍵幀，隨后得出主顏色序列，並對主顏色序列進行方差統計和聚類顏色比率的計算，最後依據比賽幀比率來進行鏡頭分類。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。分享友人