最鄰近分類法 的英文怎麼說

中文拼音 [zuìlīnjìnfēnlèifǎ]
最鄰近分類法 英文
nearest neighbor classification
  • 最 : 副詞（表示某種屬性超過所有同類的人或事物） most; best; worst; first; very; least; above all; -est
  • 近 : Ⅰ形容詞1 （空間或時間距離短） near; close 2 （接近） approaching; approximately; close to 3 （親...
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 法 : Ⅰ名詞1 （由國家制定或認可的行為規則的總稱） law 2 （方法; 方式） way; method; mode; means 3 （標...
  • 鄰近 : nearclose toadjacent to
  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. First, realized a wegener - willie distribute based network traffic anomaly detection algorithm. we make use of wegener - willie distribute to analyze the inherent time - frequency distribution characteristics of the traffic flow signal. then according to the experience of analysis on historical flow, we construct a normal flow training sample aggregation and a abnormal flow training sample aggregation

    通過魏格納-威利分佈分析網路流量信號在時頻分佈上所反映出的內在特點，根據歷史流量的經驗構造正常流量和異常流量兩個訓練樣本空間，通過k最近鄰分類演算法將帶檢測流量信號的時頻分佈與訓練樣本進行比較，完成對檢測樣本的自動分類識別。
  3. In this thesis, the method of similar estimation ( the nearest neighbor ) is applied to classify the feature character

    在特徵字的分類過程中，採用了相似形度量（最近鄰法）的方法。
  4. This study employed six data mining methods, including logistic regression, discriminant analysis, artificial neural networks, k - nearest - neighbors, na ? ve bayes classifier, and classification trees, to find the most important factors of earthquake - caused landslide

    本研究利用六種資料探勘方法，包括邏輯回歸、判別分析、類神經網路、最近鄰法、貝氏分類器、分類樹，探討影響地震引起山崩的重要因子。
  5. Phylogenetic relationships among these haplotypes were inferred from a minimum spanning network, which was constructed by the computer software minispnet, and two phylogenetic reconstructions were determined by using maximum likelihood algorithm incorporated in the phylogenetic inference package ( phylip ) version 3. 5c and neighbour joining algorithm incorporated in the software molecular evolution genetic analysis ( mega ) version 2. 0. all these methods exclusively divided the haplotypes into three monophyletic clades corresponding to china mainland, northern japan, and southern japan populations respectively. in these populations, the china mainland population and the southern japan population have a relatively closer affinity than either of them with the northern japan population

    最小跨度網路圖（ minimumspanningnetwork ， msn ）和基於最大似然法（ maximumlikelihood ， ml ）和鄰接法（ neighborjoining ， nj ）的系統發生分析均把單元型聚類為對應于中國大陸、日本南部和日本北部的三個單系，其中中國大陸和日本南部梅花鹿有相對較近的親緣關系，支持日本梅花鹿的祖先通過至少兩個大陸橋從亞洲遷移到日本的觀點。
  6. At last, svm algorithm has been applied to remote sensing image classification. compared with k near neighbor and adaptive min - distance algorithm, the experience result presents that svm algorithm has better classification effect. and the experience result also shows us that svm algorithm has good application foreground in the aspect of remote sensing image classification

    最後將svm演算法應用到遙感圖像的分類，通過與自適應最小距離法和k近鄰法的實驗結果進行比較，得出svm演算法具有更好的分類效果，也說明了svm演算法在遙感圖像分類方面具有很好的應用前景。
  7. The classifier with ability of feature selection is studied to prepare for face cascade representation and to make it possible to detect and recognize face fast and accurately. finally, construction of an array of classifiers is researched, and an effective method to design classifiers of fast face detection and recognition with complex background is presented, which is able to radically discard redundant areas and realize a robust real - time face detection designed for complex background and recognition system with large face database. finally, a fast face detection and recognition system for images with complex background is proposed and implemented by combining face cascade representation and classifier design

    首先研究了在人臉檢測和識別中常用的分類器，比如符號函數、最近鄰、神經網路、 svm 、 adaboost等，選擇了適合於人臉檢測和識別的分類器，並提出了結合pca特徵和rbf進行人臉姿態的判別方法：其次研究了具有特徵選擇功能的分類器發計，這為人臉的級聯表示提供了條什，也為快速準確的人臉檢測和識別提供了可能；最後，對組合分類器設計進行了研究，提出了適于復雜背景下快速人臉檢測和識別的有效分類器設計方案，這使得人臉檢測和識別能夠快速剔除不感興趣區域，為復雜背景下實時人臉檢測和大型人臉庫的快速識別提供了可能。
  8. 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。
  9. Because the traditional collaborative filtering recommendation has certain insufficiency such as recommendation precision, the data processing efficiency, this article proposes a collaborative filtering method based on cluster and project forecast in coordination. after the users and the commodities are carried into gathers, the people of the same kind and the commodity of the same sort should be constructed the

    由於傳統協同過濾推薦在推薦精度、數據處理效率都有一定的不足，文中提出一種基於聚類和項目預測的協同過濾方法，把用戶、商品進行聚類后，將同屬一類的用戶、商品構建用戶? ?商品子矩陣，在該矩陣基礎上進行最近鄰查詢，從而計算用戶對未評分項目的預測評分。
  10. Cluster algorithm of go groups based on mathematical morphology

    基於數據分區的最近鄰優先聚類演算法
  11. 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。
  12. In section 4. 2 we analyze its main idea and algorithm in detail, two relevant theorems included ; section 4. 3 provides plenty instances so to explain its nonlinear dimension reduction ability, section 4. 4 propose a combined method that integrates the advantage of various methods. in section 4. 5 we analyze some significant problems in lle, including the locality of manifold representation, the choice of the neighborhood, the intrinsic dimension estimation and the parametric representation of mapping. in section 4. 6 we design an algorithm for estimating the intrinsic dimension in the base of locally linear approximation and discuss the choice of its parameters

    第四章是本文的重點內容，研究一種全新的非線性降維方法? ?局部線性嵌入方法，對它的思想和演算法進行了詳細的分析，給出演算法兩個相關定理的證明；第三節對比主成分分析，通過實例說明局部線性嵌入方法的非線性降維特徵；第四節在此基礎上提出了旨在結合兩者優勢的組合降維方法；第五節提出了局部線性嵌入方法中存在的若干關鍵性問題，包括流形的局部性、鄰點的選擇、本徵維數的估計和降維映射的表示，第六節基於局部線性近似的思想提出了一種本徵維數的估計方法，設計了實用演算法，結合實例對演算法中參數的選取進行了討論；最後一節提出了一種基於局部線性重構的圖形分類和識別方法，將其應用於手寫體數字的圖像分類識別實驗，實驗得到的分類準確率達96 . 67 。
  13. 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。
  14. 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。
  15. The classifiers of nearest feature line and nearest feature plane share the same drawback in terms of the computation complexity under large data sample size and high dimensionality. therefore, a new search strategy based on locally nearest ne.

    針對最近特徵線nfl與最近特徵平面nfp分類器在大數據樣本量與高維數時計算復雜度大的問題，依據局部最近鄰準則，提出了一種新的搜索策略，使這兩種分類器在保持較高識別率的同時，提高了演算法的實時性能。
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