rough classification 中文意思是什麼

rough classification 解釋
粗分級
  • rough : adj 1 粗糙的 （opp smooth）; 凹凸的，崎嶇不平的 （opp level）。2 粗毛的，多毛的，蓬亂的（頭發）。...
  • 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. 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。
  2. Rough set classification algorithm based on pl sql

    的粗糙集分類演算法研究
  3. In this paper, we propose a novel approach using sentential frequent itemset, a concept comes from association rule mining, for text classification, which views a sentence rather than a document as a transaction, and uses a variable precision rough set based method to evaluate each sentential frequent itemset s contribution to the classification

    為了解決這一問題，參考目前的數據挖掘領域的工作，提出了一個文檔數據庫模型，即將每一篇文檔映射為一個文檔數據庫，文檔中的每個句子看作數據庫中的一個交易，每一個詞看作一個項目。
  4. 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。
  5. In this paper, our work begins with the classification of indefinite information presented in the first chapter and these classes include randomicity, fuzziness, non - partitionality, incomparability, incompleteness and unreliability. and the corresponding theories and methods of studying these information are simply described. then the following chapter of this paper discusses the approximate processing of rough boundary region - sort of problem of non - partitionality

    本文第一章從不確定問題的定性描述出發，將不確定性信息分為了隨機性不確定性信息、模糊性不確定信息、不可區分性不確定信息、不可比較性不確定性信息、不完全性不確定性信息和不可靠性不確定信息，並對各種不確定信息的產生和處理方法進行了簡單的概述。
  6. It is helpful to begin with a rough and ready classification

    首先進行粗略的分類是很有幫助的。
  7. 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。
  8. 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。
  9. 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。
  10. 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。
  11. 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。
  12. Significance of expansive soil classification indexes analysed by rough sets

    膨脹土路基的脹縮變形模型試驗
  13. At the aspect of preprocess, some preprocess methods are studied and improved, including rough set, data clustering, concept hierarchies and language field, etc. at the aspect of mining algorithms, classification is an important knowledge discovery method

    在數據的預處理方面，主要研究粗集理論、數據聚類、概念樹、語言場等預處理方法。在挖掘模型與演算法的選取中，分類是一種重要的知識發現方法，它能以簡潔的模型預測新到達對象的類別。
  14. The rough set theory ( rst ), which was introduced by z. pawlak in 1982, is a tool to deal with vagueness and uncertainty. its main idea is inducing decision or classification rule through knowledge reduction by keeping the classify ability

    粗糙集理論是波蘭數學家z . pawlak於1982年提出的一種處理不確定和不精確數據的理論，其主要思想是在保持分類能力不變的前提下，通過知識約簡，導出問題的決策或分類規則。
  15. In inconsistent decision system two improved algorithms namely decision conception conclusion and rough repetition groups are put forward to mine the classification rules with certainty reliability

    在不相容決策系統中提出了兩種改進演算法即決策概念包含法和粗糙重復組法對不相容的決策系統挖掘出具有一定可信度的分類規則。 2
  16. 20 this is a very rough classification. obviously, there are further steps, such as line breaking, alignment and justification. they need not be discussed here, as they go beyond localization

    20這是一種粗糙的分類。顯然，還有更多的步驟，例如斷行、對齊。由於它們超出了本地化的范圍，所以不在這里討論。
  17. In this paper preprocessing, feature extraction and selection, and rough classification are performed on then chromosome images. some experiments are made to verify the effectiveness of the methods presented

    本文對染色體圖像進行了預處理，特徵提取與選擇和初步分類分析，通過大量的實驗證明我們所採用的演算法是有效的。
  18. 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。
  19. 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。
  20. 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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