nearest neighbor classification 中文意思是什麼

nearest neighbor classification 解釋
最近鄰域分類
  • nearest : ad. 最近的，最親近的
  • neighbor : n 1 鄰人 鄰居；鄰近的人；鄰國（人）。2 鄰座（的人）；鄰接的東西。3 同胞；世人。4 （對任何不知姓名...
  • 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. A variety of methods including the tabular comparison of data, the tabular comparison of similarity coefficient, the nearest neighbor method and the group - average method of hierarchical agglomerative classification were applied to investigate the forest communities in meizi lake area

    森林植被樣地中以喬木層樹種的重要值為指標，採用紙條排隊法、群落相似系數分類法、最近鄰體法、組平均法對梅子湖森林植被樣地進行數量分類。
  2. The results are compared with the genetic algorithm in combination with the k - nearest neighbor ( knn ) classification rule

    最後，將比較的結果再與基因演演算法結合k個最近鄰法進行比較。
  3. When the discount coefficient is 1 and all weights of the nearest neighbor sample points are the same, the k - nn classification method based on evidence reasoning model will become the k - nn classification method based on evidence theory

    並且當折扣系數為1 ，且給定所有最近鄰樣本點權重相等時，基於證據推理模型的k - nn分類方法就成為基於證據理論的k - nn分類方法。
  4. In present methods of track - to - tack correlation, the false and lost track - to - track correlation have not been taken into account in the complex background with dense targets. so two kinds of methods are proposed to deal with this problem, one of which is the correlation algorithm based on fuzzy synthetic decision and d - s evidence theory, another is based on k - nearest neighbor ( k - nn ) principle and d - s evidence theory. the two methods combine the logic of fuzzy decision and strictness of statistical classification with intelligence of evidential theory successfully

    目前的航跡關聯方法在密集目標環境下，航跡錯關聯概率和漏關聯概率較大，針對這一問題，本文利用d - s證據理論，提出了基於模糊綜合決策的d - s航跡關聯方法和基於k近領域的d - s航跡關聯方法，兩種方法成功地將模糊決策的邏輯性和統計模式分類的嚴密性與證據理論的智能特性相結合，模擬結果說明了兩種方法的有效性和實用性。
  5. During the former classification method, the classification expert gives the weights of the nearest neighbor sample points of the sample point to be classified, then defines the key sample point and non - key sample point, furthermore gives their support degree, discount coefficient

    在前一種分類方法中，分類專家對待分類樣本點的最近鄰樣本點給出權重，從而定義關鍵樣本點及非關鍵樣本點，進而給出它們的支持度、折扣系數。
  6. A method based on fuzzy equivalence relation is applied to implement target classification and a synthetic algorithm is presented to fulfill multi - layer structure among groups by using the nearest - neighbor method and field knowledge

    應用基於模糊等價關系的方法實現目標編群，並提出一種基於域知識和最近鄰法相結合的演算法來實現群結構遞增形成的策略。
  7. 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。
  8. These technologies include the procedure of classification and the preprocessing of classification data and compared and evaluated criterion of classification methods. several of typical classification algorithms are compared which are decision - tree and k - nearest neighbor and neural network algorithm. then the emphasis of the paper is induced that divide the classification to eager and lazy and the research of classification algorithm in data mining is based on this divide

    討論了數據挖掘中分類的基本技術，包括數據分類的過程，分類數據所需的數據預處理技術，以及分類方法的比較和評估標準；比較了幾種典型的分類演算法，包括決策樹、 k -最近鄰分類、神經網路演算法；接著，引出本文的研究重點，即將分類演算法劃分為急切分類和懶散分類，並基於這種劃分展開對數據挖掘分類演算法的研究。
  9. The optimized feature set feeds a 3 - class classification module, which is based on the traditional binary svm classifier. and the proposed linear programming svm reduces the burden of the svm classifier and improves its learning speed and classification accuracy. a new algorithm that combined svm with k nearest neighbor ( knn ) is presented and it comes into being a new classifier, which can not only improve the accuracy compared to sole svm, but also better solve the problem of selecting the parameter of kernel function for svm

    在研究了數據挖掘、支持向量機及其有關技術的基礎上，建立了實現三類水中目標識別的svm方法；採用線性規劃svm解決了傳統二次規劃svm在海量樣本情況下導致的時間和空間復雜度問題；提出了將最近鄰分類與支持向量機分類相結合的svm - knn分類器應用於水中目標識別的思想，較好地解決了應用支持向量機分類時核函數參數的選擇問題，取得了更高的分類準確率。
  10. The often - used classification is classification by decision tree induction, bayesian classification and bayesian belief networks, k - nearest neighbor classifiers, rough set theory and fuzzy set approaches

    分類演算法常見的有判定樹歸納分類、貝葉斯分類和貝葉斯網路、 k -最臨近分類、粗糙集方法以及模糊集方法。
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