similarity matrix 中文意思是什麼

similarity matrix 解釋
相似矩陣
  • similarity : n. 1. 類似，相像，相似。2. 類似點；類似物，相似物。
  • matrix : n （pl matrices 或matrixes）1 【解剖學】子宮；母體；發源地，策源地，搖籃；【生物學】襯質細胞；間...
  1. Sixties primers were used for dna amplification and a total of 237 amplification products range from 450bp to 2500bp were generated. a similarity matrix for all pairwise comparisons was calculated using nei and li ' s formula and then transformed to distance matrix. dendrograms were constructed by applying unweighted pair - group arithmetic average ( upgma ) and neighboring - jointing cluster analyses using the phylip software

    在第二部分，應用改進的ctab法提取了石蓴屬和滸苔屬各3個種及作為對照的剛毛藻的基因組dna ， 60個引物被用於擴增，共獲得237個片段大小在450 - 2500bp之間的擴增片段，依據nei和li （ 1979 ）的公式計算出樣品成對比較的相似性距陣並換算成遺傳距離，應用phylip軟體包，按照upgma法和n - j法分別構建聚類圖。
  2. In the preprocessing stage the method of user and session identification often adopt heuristic algorithm for the being of cache and agent. this induce the uncertainty of data resource. the cppc algorithm avoid the limitation and has no use for complicated hash data structure. in this algorithm, by constructing a userld - url revelant matrix similar customer groups are discovered by measuring similarity between column vectors and relevant web pages are obtained by measuring similarity between row vectors ; frequent access paths can also be discovered by further processing of the latter. experiments show the effectiveness of the algorithm. in the fourth part, this thesis bring some key techniques of data mining into web usage mining, combine the characteristic of relation database design and implement a web usage mining system wlgms with function of visible. lt can provide the user with decision support, and has good practicability

    本文演算法避免了這個缺陷，且不需要復雜的hash數據結構，通過構造一個userid - uel關聯矩陣，對列向量進行相似性分析得到相似客戶群體，對行向量進行相似性度量獲得相關web頁面，對後者再進一步處理得到頻繁訪問路徑。實驗結果表明了演算法的有效性。第四是本文將傳統數據挖掘過程中的各種關鍵技術，引入到對web使用信息的挖掘活動中，結合關系數據庫的特點設計並實現了一個具有可廣西人學頎士學位論義視化功能的web使用挖掘系統wlgms 。
  3. In this dissertation, with the aid of many types of constructive transformations and symbolic computation, some topics in nonlinear waves and integrable system are studied, including exact solutions, painleve integrability, backlund transformation, darboux transformation, symmetry ( similarity reduction ), conditional symmetry, lax integrable hierarchy, liouville integrable n - hamilton structure, constraint flow, involutive system, lax representation, r - matrix, separation of variables and integrable couplings. chapter 2 and 3 are devoted to investigating exact solutions of nonlinear wave equations : firstly, the basic theories of c - d pair and c - d integrable system are presented

    本文以構造性的變換及符號計算為工具，來研究非線性波和可積系統中的一些問題：精確解（如孤子解、周期解、有理解、 dromion解及compacton解等） 、 panileve可積性、 backlund變換、 darboux變換、對稱（相似約化） 、條件對稱、 lax可積族、 liouville可積的n - hamilton結構、約束流、對合系統、 lax表示、 r -矩陣、變量分離及可積的耦合系統
  4. 3. according to the spline theory we presented a shape matching algorithm based on the similarity matrix of curvature and torsion values of 3d curve, we reduced the 3 - d curve matching task into a 1 - d string matching problem, which makes the matching more veracious and can be used on the 2d or 3d curve matching. in order to reduce the cost of matching, we used multiple scale technique

    依據樣條曲線的基本理論，研究了基於b樣條的輪廓曲線的匹配方法，給出了由輪廓曲線不變量曲率和撓率構造的相似不變量的選取以及基於相似矩陣的匹配演算法，並對該演算法的時間復雜度作了估計，同時，將多尺度技術引入到物體輪廓的匹配問題中。
  5. Thirdly, similarity matrix, dissimilarity matrix or similarity table are established based on the n - strong peaks, the overlap rate of common peaks and the cosine / sine of vectors " angle which are derived from the fingerprint chromatograms of samples. and based on these data model, clustering research has been done by k - means algorithm, biggest tree in fuzzy clustering and improved cobweb algorithm, where different results have been gained. by comparing, cobweb algorithm is the best

    本次研究利用n強峰、共有峰的重疊率和向量夾角正餘弦值對樣品色譜指紋圖譜分別建立了相似度矩陣、相異性矩陣或相似度表，以這些數據模型為基礎，分別用了k -平均、模糊聚類的最大樹法和改進的cobweb法進行了聚類研究，得到了不同的效果。
  6. In this dissertation, we use a feature matrix and a semantic relevance matrix which is established by long - term learning the log of the feedback offered by users, then optimize the semantic relevance matrix, and finally, combine the lower - feature matrix and semantic relevance matrix to retrieve images. this approach achieves the estimation of the similarity between

    對于某些特殊情況，僅僅依靠修改特徵相似度不能起到很明顯的效果，由此本文引入了語義關系矩陣，先通過對反饋日誌的長期學習建立語義關系矩陣，之後再對語義關系矩陣進行優化，實現了同時被標注為負反饋的圖像之間相似度的估計。
  7. And then, a novel simility measure called topology similarity is proposed in the paper. a clustering model is built based on this simility measure, which take good use of the concept, topology similarity matrix

    再次，論文提出一種新的相似性度量標準:拓撲相似性，使用該相似性標準為聚類問題建立數據模型，並建立拓撲相似性矩陣表示該模型。
  8. It researches the background model based on gradient and chromaticity which can update background in real - time, which provide exact information of objects to next module. in tracking objects module, the thesis applies kalman filter to estimate and predict the objects ’ moving state. in association module, the mahalanobis distance is utilized to get the similarity among objects. “ matching matrix ” is presented to get the best matching object

    研究了基於色度、梯度建立背景模型檢測目標的方法，能實時更新背景模型，基本避免場景中光線變化的影響，有效消除了目標陰影，能準確檢測目標，並得到目標輪廓及提取視頻目標，為下一階段針對目標區域的處理提供較完整的目標信息。
  9. Compare a lot of face image characteristic vector with face image sets characteristic matrix in order to get their similarity, and find the least value of similarity as threshold. in the detecting phase, compute the similarity between characteristic vector of testing region in gray image and face image sets characteristic matrix, if the similarity bigger or equal to threshold then the testing region is a human face, otherwise is not

    然後，用大量的人臉圖像的特徵向量與人臉圖像集特徵矩陣比較它們的相似程度，找出值小相似度，並把這個最小相似度作為閾值；在檢測階段，求出灰度圖像的待測區域的特徵向量與人臉特徵矩陣的相似度，若該相似度大於等於閾值，則是人臉，否則不是人臉。
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