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variance contribution in Chinese

Pronunciation:
How to pronounce "variance contribution""variance contribution" in a sentence

Translationmobile phoneMobile

  • 方差貢獻

Examples

  • Regarding the variance contribution rate of each factor as right count weight , this thesis gets the evaluation score and rank
    在計算綜合得分時是以因子的方差貢獻率為權數加權求和，由此得到各地區(qū)的綜合得分以及排名。
  • Spca arithmetic has smaller number of spectral principal components and greater variance contribution than pca by choosing proper kernel functions and parameters
    當選取合適的核函數和參數時，譜主成分的個數比主成分的個數要小且累積方差貢獻率要大。
  • The results of numerical calculations show that : the number of spectral principal component and cumulate variance contribution are different its depending on kernel functions
    通過數值例子計算表明：取不同核函數而得到的譜主成分分析，其譜主成分的個數及累積方差貢獻率是有差別的。
  • Via numeric sample analysis , it is found that evaluation functions are constructed by weighing principal components for pca . however , evaluation functions can be quite different when there are more than three principal components and characteristic vectors other than first one are chosen in different directions . for spca , variance contribution can be greater than 90 % by selecting just one principle component
    將譜主成分分析應用于多指標評價系統中，通過數值例子分析：主成分分析是通過對各個主成分加權構造評價函數，當主成分個數不小三個時，從第二個特征向量開始，對方向的不同選取，可導致評價函數的極大差異：而用譜主成分分析，能做到只取一個譜主成分就可使方差貢獻率大于90 。
  • Therefore , spca gotten via selecting polynomial kernel functions is more accurate than pca in multi - index evaluation system , and has fewer dimensions . comparatively , for spca using gauss kernel function and laplace kernel function , it is required to normalize original data according to the category , and the constructed evaluation functions are better than the ones constructed via using pca . however , if variance contribution of first principal component of pca is more than 85 % , evaluation function curves of spca and pca are similar
    在作多指標評價中，選用多項式核函數而得到的譜主成分分析，比主成分分析得到的主成分具有維數低且精度高的優(yōu)點；而用gauss核函數和laplace核函數的譜主成分分析，需對原數據作同類別數據間的規(guī)范化，其構造的評價函數也優(yōu)于用主成分方法構造的評價函數。
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