chinese character classification 中文意思是什麼

chinese character classification 解釋
形聲
  • chinese : adj. 中國（人）的；中國（話）的。 the Chinese Wall 萬里長城。n. 〈sing. , pl. 〉 中國人；中國話，漢語。
  • character : n 1 性格，品格；特性，性狀，特徵；【生物學】形質。2 身分，地位，資格。3 名聲，聲望。4 （戲劇、小...
  • classification : n 1 選別；分等，分級；分選。2 【動、植】分類（法）。 〈分類級別為： phylum 【動物；動物學】及 div...
  1. This article discussed the chinese word slice, character extraction, character expression and character matching methods, and established the chinese text classification and clustering algorithms based on neural network. in the design of chinese text mining based on web, the paper analyzed and researched the expression of web page information, structure feature, web page control symbol and html control symbol, and built the extraction flow of web page information, then gave two concrete application of chinese text mining based on web through combining with practical problems

    討論了文本分類中的中文詞切分、特徵提取、特徵表示、特徵匹配方法，建立了基於神經網路的中文文本分類、聚類演算法，在web中文文本信息挖掘的設計中，對網頁信息的表示、結構特點、網頁控制符、 html控制符號處理進行了詳細分析與研究，構建了網頁信息提取流程，並結合實際問題，給出了web環境下中文文本信息挖掘的兩個具體應用。
  2. ( 2 ) the influence to classification result is highly effected by using different classifier, for example, the center - vector algorithm obtains better classification results than other two algorithms. with the character feature, the average recall is 80. 73 %, and the average precision is 82. 94 %, and with the chinese - word feature, the average recall is 83. 6 %, and the average precision is 85. 97 %. different corpuses influence the classification result. for example, the average recall is 89. 31 % and the average precision is 88. 33 %, by using the news web pages as corpus from the web site " www. sina. com. cn ", which adopt the center - vector algorithm to structure classifier and select chinese - word as feature

    對三種分類器分別以字、詞為特徵進行分類測試、分析發現：使用相同的分類演算法，用詞作為特徵項，比以字作為特徵的分類效果好；用不同的演算法構造分類器對分類效果的影響很大，如中心向量演算法在字、詞特徵下的分類效果優于其他兩演算法；在以字為特徵的情況下，該演算法的平均查全率80 . 73 ，平均查準率82 . 94 ；在以詞為特徵的情況下，該演算法的平均查全率83 . 6 ，平均查準率85 . 97 ；選用語料不同對分類效果也有影響，如用新浪網（ www . sina . com . cn ）網頁語料進行測試，使用中心向量法分類器和詞作為特徵的情況下，平均準確率為89 . 31 ，平均查全率為88 . 33 。
  3. Character classification method based on component character for chinese character recognition

    基於部件特徵的分類方法及在漢字識別中的應用
  4. 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。
  5. 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。
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