classification generalization 中文意思是什麼

classification generalization 解釋
分類綜合
  • 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...
  • generalization : n. 1. 一般化，普遍化。2. 概括，綜合，總結；歸納；法則化。3. 廣義；概說；概念，通則。
  1. The traditional decision tree, which was represented by id3 proposed by quinlan in 1986, can solve the classification problem well. but when the class number increases, the produced single decision tree becomes complex and generalization capability decreases

    以quinlan1986年提出的id3為代表的傳統決策樹能較好的解決分類問題，但當類的個數增多時，所產生的單一決策樹就會變得復雜同時概括能力降低。
  2. In order to scientisfic classification, according to four diffierent standards, the classifications of thematic maps form were put forword. visual effect scheme including line form, image form and three - dimensional form. map layout scheme including main unit form and multi - unit form. scheme of generalization degree of contents, it contains analytical form and integrated form including compound form and synthetic form. scheme of conetnet framework and means of representation

    為科學分類，依據4種不同標志，提出了專題地圖4種分類方案：視覺感受方案：線劃圖型、影像圖型和立體圖型；圖面結構方案：主單元和多單元圖型；內容概括程度方案：綜合與分析圖型，其中綜合圖型又分組合型與合成型；內容結你與表示方法方案：等量線圖型、向量線圖型、統計圖型、分布圖圖型、類型圖圖型和區劃圖圖型。
  3. When we design the classification, we combine the tree classification and the support vector machines in order to improve the ability of combining experiences and performance of generalization

    在模式識別的分類器設計上，我們採用了樹分類器和支持向量機相結合的方法，提高了分類器經驗結合的能力和泛化能力。
  4. At the same time, this paper puts forward a validity function for judging clustering in order to lead us to use it in k - nearest neighbor classification ; then introduces " generalization capability of a case " to k - nearest neighbour. according to the proposed approach, the cases with better generalization capability are maintained as the representative cases while those redundant cases found in their coverage are removed. we can find a new less but almost complete training data set, consequently reduce complexity of seeking near neighbour

    針對k值的學習，本文初步使用了遺傳演算法選擇較優的k值，同時總結了一種聚類有效性函數，數值實驗證實了其有效性，旨在指導應用於k -近鄰分類中；然後還將「擴張能力」的概念引入k -近鄰演算法，根據訓練集例子不同的覆蓋能力，刪除冗餘樣本，得到數量較小同時代表類別情況又比較完全的新的訓練集，從而降低查找近鄰復雜性。
  5. As a currency learning techniques introduced recent years, svms which handle small sample size problem have the features of good generalization ability, solid theoretical background, high accuracy, and getting global optimization. ho vever, it is a classifier for two - class originally and not suite for the multi - class problems and dealing with large data sets. on the other hand, rst has the features of handling and reducing large data sets while has lower classification accuracy than svms. in this paper, the data are classified in advance with the rst, and two methods of combination of the data to classify two - class problems are proposed

    但它是二值分類器，不適用於多值分類場合及處理海量數據。粗集理論則具有處理和約簡大數據量的優勢，但分類精度不如svm方法。本文利用粗集理論對數據進行預分類，在此基礎上提出兩種二值分類數據組合方法，然後，再利用svm兩兩分類。
  6. Based on classification and included relation of concept extension, the authors give the mathematical model of quality character generalization

    摘要在分類的定義下，根據概念外延的包含關系，給出了質量特徵概括的數學模型。
  7. The convergence of the pnn decision rule for the bayesian decision rule is proved with probability. an explicit formulation of pnn generalization ability is proposed. and the accuracy rate of a classification network is explained as the maximum likelihood estimation of the generalization ability

    證明了pnn的決策函數依概率收斂于貝葉斯決策函數;給出了pnn的推廣能力表達式;證明了一個分類網路的測試集正確率是該網路推廣能力的極大似然估計;給出了分類網路中需要的測試集數目表達式;證明了pnn推廣能力不大於由貝葉斯決策所帶來的正確識別率
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