高維空間 的英文怎麼說

中文拼音 [gāowéikōngjiān]
高維空間 英文
higher dimensional space
  • 高 : Ⅰ形容詞1 （從下向上距離大; 離地面遠） tall; high 2 （在一般標準或平均程度之上; 等級在上的） above...
  • 維 : Ⅰ動詞1 （連接） tie up; hold together; link 2 （保持; 保全） maintain; safeguard; preserve; keep ...
  • 空 : 空Ⅰ形容詞（不包含什麼; 裏面沒有東西或沒有內容; 不切實際的） empty; hollow; void Ⅱ名詞1 （天空） s...
  • 間 : 間Ⅰ名詞1 （中間） between; among 2 （一定的空間或時間里） with a definite time or space 3 （一間...
  • 高維 : higher-dimension高維空間 higher dimensional space; higher space
  • 空間 : space; enclosure; room; blank; interspace
  1. As we all known, with the founding of euclidean geometry in ancient greece, with the development of analytic geometry and other kinds of geometries, with f. kline " s erlanger program in 1872 and the new developments of geometry in 20th century such as topology and so on, man has developed their understand of geometry. on the other hand, euclid formed geometry as a deductive system by using axiomatic theory for the first time. the content and method of geometry have dramatically changed, but the geometry curriculum has not changed correspondingly until the first strike from kline and perry " s appealing

    縱觀幾何學發展的歷史，可以稱得上波瀾壯闊：一方面，從古希臘時代的歐氏綜合幾何，到近代解析幾何等多種幾何的發展，以及用變換的方法處理幾何的埃爾朗根綱領，到20世紀拓撲學、高維空間理論等幾何學的新發展，這一切都在不斷豐富人們對幾何學的認識；另一方面，從歐幾里得第一次使用公理化方法把幾何學組織成一個邏輯演繹體系，到羅巴切夫斯基非歐幾何的發現，以及希爾伯特形式公理體系的建立，極大地發展了公理化思想方法，不管是幾何學的內容還是方法都發生了質的飛躍。
  2. And then, multiple - dimention asmptotic periodic function space is still banach space

    而且把結論推廣到高維空間，也會得到漸近周期函數空間是banach空間
  3. Svm maps input vectors nonlinearly into a high dimensional feature space and constructs the optimum separating hyperplane in the spade to realize modulation recognition

    支撐矢量機把各個識別特徵映射到一個高維空間，並在高維空間中構造最優識別超平面分類數據，實現通信信號的調制識別。
  4. Outlier detection is a very important technique in data mining. in this paper, a more practicable system on outlier mining technique in high - dimensional is presented, which uses reduction character of rough set to cut out some inessential attributes and then mines outliers in subspace of every correlation rules

    本文提出了對高維空間下離群點挖掘技術上的一個改進，即利用粗糙集的約簡特性對高維空間下的數據屬性進行約簡，通過約簡一些無關緊要的屬性來減少高維空間的維數。
  5. By mapping input data into a high dimensional characteristic space in which an optimal separating hyperplane is built, svm presents a lot of advantages for resolving the small samples, nonlinear and high dimensional pattern recognition, as well as other machine - learning problems such as function fitting

    Svm的基本思想是通過非線性變換將輸入空間變換到一個高維空間，然後在這個新的空間中求取最優分類超平面。它在解決小樣本、非線性及高維模式識別問題中表現出許多特有的優勢，並能夠推廣應用到函數擬合等其他機器學習問題中。
  6. The maximum distance in high dimensional spaces

    高維空間的最大距離
  7. The advent of dna micro - array make it possible to perform gene diagnosis and gene treatment. gene selection is one of the major challenge of gene - chip technology, for gene diagnosis where only a gene subset is enough for diagnosis of diseases, for resolution of curse of dimensionality which occurs especially in dna microarray dataset where there are more than thousands of genes and only a few number of experiments ( sample )

    基因晶元的出現為基因診斷和基因治療提供了很好的前提和可能性，超高維空間超小樣本的基因選擇問題是基因晶元技術的挑戰性課題之一，對于解決維數發難問題和獲得診斷基因具有重要的理論和實際意義。
  8. Searching in high - dimensional spaces : index structures for improving the performance of multimedia databases. acm computing surveys, 2001, 33 : 322 - 373. 2 guttman a. r - tree : a dynamic index structure for spatial searching

    本文試圖將高維空間中的每個點對應的兩種距離尺度通過線性組合的方式表達成復合距離尺度，從而能夠較好地縮小查詢空間，提高查詢效率。
  9. Thirdly, the optimization performance of mec in the high - dimension space is tested

    接下來，測試了高維空間中mec的優化性能。
  10. This idea can also be applied to the higher dimensional point sets

    該演算法適用於任何類型的平面子集，並且可以推廣到高維空間子集的k ?近鄰計算中。
  11. Experimental study has been carried out with this two - level mec algorithm in high - dimension space

    使用該雙層mec演算法進行mec高維空間的性能的實驗研究。
  12. As analysis of data shows, this algorithm can find the outliers in high - dimensional space effectively

    數據分析表明，該演算法能有效地發現高維空間數據集中的離群點。
  13. In dimensions two and higher, wavelets can not efficiently represent object with discontinuities along edges

    摘要在二維或更高維空間中，小波不能有效地表達沿邊緣間斷的物體。
  14. The focus of this paper is on the issue of minimizing cpu time to handle high speed data streams on top of the requirements of high accuracy and small memory

    在本篇論文中，我們研究高速產生的數據流中k中值點k - median的快速估計，特別是這些數據來源於高維空間。
  15. 2. some theory of high - dimension space is introduced and a new feature extraction method avito correlation - angle is presented based on high - dimension space geometry. 3

    介紹了高維空間幾何學的一些基本定義，並在此思想上，提出了自相關夾角法對語音信號進行特徵提取。
  16. Essentially, a key problem on the interpolation by multivariate splines is to study the piecewise algebraic curve and the piecewise algebraic variety for n - dimensional space rn ( n > 2 )

    本質上，解決多元樣條函數空間的插值結點的適定性問題關鍵在於研究分片代數曲線，在高維空間里就是研究分片代數簇。
  17. The mapping the pilot training from the lower dimension space to high dimension space, and in the high dimension space, the wave - let kernel function is adopted, according to recursion least square criteria, the lower linear inseparable problem is convert to the separable problem

    然後將根據結構風險遞歸二乘最小化準則回歸估計支持向量機原理，把導頻訓練序列映射到高維空間，並在高維空間採用結構小波核函數，達到了將低維空間的非線性估計轉化為高維空間的線性估計的目的。
  18. In the first and second section of chapter 1 we introduce the model of dimension reduction problem, put forward the concepts of dimension - reduction function and embedding function, and make a classification for the dimension reduction problem ; in section 1. 3 we discuss " the curse of dimension " and the sparsity of high - dimensional space ; in section 1. 4 we discuss " intrinsic dimension " and its estimation based on the model of dimension reduction

    第一章首先提出了降維的模型和定義，討論了相關的問題；第三節討論「維數禍根」現象和高維空間的稀疏性，通過實例分析其對高維空間的數據分佈特性具體影響；第四節討論了本徵維數及其估計的基本問題。
  19. This dissertation mainly discusses issues on the applications of wavelet analysis in the control of nonlinear systems. the main topics are : ( 1 ) the problem of modeling and identification of nonlinear systems in the high dimensional space using wavelet analysis is studied

    本文對小波分析在非線性系統控制中應用的若干問題作了較深入地研究和探索，主要的研究內容包括： （ 1 ）研究了小波分析在高維空間中對非線性系統建模的問題。
  20. Based on high - dimension space geometry, every speech sample is looked as a point in space. then the speech sample point is extracted feature by lpc, mel - scaled cepstrum analysis or auto correlation - angle. their feature is looked as a point too

    基於高維空間幾何的思想，把每個樣本點和其特徵值看作高維空間中的一個點，用線性預測分析、 mel倒譜分析和自相關夾角法對樣本點提取特徵，然後用點在空間的投影來判別語音和非語音，根據判別結果來比較三種特徵提取方法的優劣。
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