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separating hyperplane in Chinese

Pronunciation:
How to pronounce "separating hyperplane""separating hyperplane" in a sentence

Translationmobile phoneMobile

  • 分離超平面

Examples

  • The separating hyperplane of traditional support vector machines is sensitive to noises and outliers
    摘要傳統(tǒng)的支持向量機分類超平面對噪聲和野值非常敏感。
  • 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征映射到一個高維空間，并在高維空間中構(gòu)造最優(yōu)識別超平面分類數(shù)據(jù)，實現(xiàn)通信信號的調(diào)制識別。
  • For this problem , a separating hyperplane is designed with the principle of maximizing the distance between two class centers , and a novel support vector machine , called maximal class - center margin support vector machine ( mccm - svm ) is designed
    為了解決這個問題，本文以兩個類中心距離最大為準(zhǔn)則建立分類超平面，構(gòu)造一個新的支持向量機，稱作類中心最大間隔支持向量機。
  • The idea is proposed that those increased date , which near the separating hyperplane , is significant for the forming of the new hyperplane , whenever these date are classed by the former hyperplane to test error set berr or test right set bok
    與傳統(tǒng)的增量學(xué)習(xí)方法不同，本文中，作者認(rèn)為那些在分類面邊緣增加的數(shù)據(jù)對分類面的改變都起著重要的作用，無論這些數(shù)據(jù)被初碩士論文支持向量機在圖像處理應(yīng)用中若干問題研究始分類器p劃分到測試錯誤集berr或者測試正確集b 。
  • 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，然后在這個新的空間中求取最優(yōu)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(xiàn)出許多特有的優(yōu)勢，并能夠推廣應(yīng)用到函數(shù)擬合等其他機器學(xué)習(xí)問題中。
  • The separating plane with maximal margin is the optimal separating hyperplane which has good generation ability . to find a optimal separating hyperplane leads to a quadratic programming problem which is a special optimization problem . after optimization all vectors are evaluated a weight . the vector whose weight is not zero is called support vector
    而尋找最優(yōu)分類超平面需要解決二次規(guī)劃這樣一個特殊的優(yōu)化問題，通過優(yōu)化，每個向量(樣本)被賦予一個權(quán)值，權(quán)值不為0的向量稱為支持向量，分類超平面是由支持向量構(gòu)造的。
  • Support vector machine is a kind of new general learning machine based on statistical learning theory . in order to solve a complicated classification task , it mapped the vectors from input space to feature space in which a linear separating hyperplane is structured . the margin is the distance between the hyperplane and a hyperplane through the closest points
    支持向量機是在統(tǒng)計學(xué)習(xí)理論基礎(chǔ)上發(fā)展起來的一種通用學(xué)習(xí)機器，其關(guān)鍵的思想是利用核函數(shù)把一個復(fù)雜的分類任務(wù)通過核函數(shù)映射使之轉(zhuǎn)化成一個在高維特征空間中構(gòu)造線性分類超平面的問題。
  • The separating hyperplane structured by support vectors . the larger data set in real world demands higher efficiency . decomposition is the first practical method to deal with larger data set . it decomposes the training set to two parts : active and inactive , the active part is called working set
    由于現(xiàn)實世界的數(shù)據(jù)量一般比較大，因此對優(yōu)化的效率要求較高，分解是第一種實用的可處理大數(shù)據(jù)集的技術(shù)，它把訓(xùn)練集分成固定大小的工作集和非工作集兩部分，每次迭代只解決一個工作集中的子優(yōu)化問題。

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