linear discriminant analysis 中文意思是什麼

linear discriminant analysis 解釋
線性判別分析
  • linear : adj. 1. 線的，直線的。2. 長度的。3. 【數學】一次的，線性的。4. 【動、植】線狀的；細長的。5. 由線條組成的，以線條為主的，強調線條的。
  • discriminant : n. 【數學】判別式。
  • analysis : n. （pl. -ses ）1. 分解，分析；【數學】解析。2. 梗概，要略。3. 〈美國〉用精神分析法治療（＝ psychoanalysis）。
  1. Theory of fisher linear discriminant analysis and its application

    線性鑒別分析的理論研究及其應用
  2. A study on personal credit scoring using linear discriminant analysis

    線性判別式分析在個人信用評估中的應用
  3. A new two - dimensional linear discriminant analysis algorithm based on fuzzy set theory

    基於模糊集理論的二維線性鑒別分析新方法
  4. The conventional principal component analysis ( pca ) and fisher linear discriminant analysis ( lda ) are based on vectors. that is to say, if we use them to deal with the image recognition problem, the first step is to transform original image matrices into same dimensional vectors, and then rely on these vectors to evaluate the covariance matrix and to determine the projector

    所提出的這兩種方法的共同特點是，在進行圖像特徵抽取時，不需要事先將圖像矩陣轉化為高維的圖像向量，而是直接利用圖像矩陣本身構造圖像散布矩陣，然後基於這些散布矩陣進行主分量分析與線性鑒別分析。
  5. Rather, in this paper, two straightforward image projection techniques, termed image principal component analysis ( 1mpca ) and image fisher linear discriminant analysis ( imlda ), are respectively developed to overcome the weakness of the conventional pca and lda as applied in image feature extraction

    在orl標準人臉庫和nust603人臉庫上的試驗結果表明，與通常的主分量分析與線性鑒別分析方法相比，圖像投影鑒別分析與主分量分析技術將特徵抽取的速度提高了一個數量級以上。不僅如此，其識別精度依然高於傳統的eigenfaces與fisherfaces方法。
  6. Feature extraction through 2 - order polynomial fit of the descending part of the response curve made possible a timesaving measurement process. the performances of two pattern recognition algorithms, namely principal component analysis ( pca ) and linear discriminant analysis ( lda ) in practical problems were discussed. artificial neural network ( ann ) was utilized with back - propagation algorithm ( bpa ), and the combination of pca / lda with ann improved the identification performance of the system

    基於對模式識別系統的深入研究，提出了從響應階段數據提取特徵的方法，節省了測試所需時間；比較了主成分分析法（ principalcomponentanalysis ， pca ）與線性判別式法（ lineardiscriminantanalysis ， lda ）兩種模式識別方法在實際應用中的不同結果，分析了原因；設計了採用誤差反傳演算法back - propagationalgorithm ， bpa ）的前向人工神經網路（ artificialneuralnetwork ， ann ） ，並指出其應用中存在的問題，提出了改進建議；利用pca lda與ann相結合的方法改善了系統的識別性能。
  7. Linear projection analysis, including principal component analysis ( or k - l transform ) and fisher linear discriminant analysis, is the classical and popular technique for feature extraction

    線性投影分析，包括主分量分析（或稱k - l變換）和fisher線性鑒別分析，是特徵抽取中最為經典和廣泛使用的辦法。
  8. We first develop a theoretical framework for the uncorrelated fisher linear discriminant analysis ( ulda ) and show it to be an improvement of the classical linear discriminant analysis in theory. we demonstrate that ulda outperforms the foley - sommon discriminant analysis ( fslda ) and discuss why it is

    該文完善了具有統計不相關性的線性鑒別分析的理論構架，給出了求解不相關的最優鑒別矢量集的一個非常簡單而有效的演算法，並指出統計不相關的線性鑒別分析的理論是經典的fisher線性鑒別法的進一步發展。
  9. The inherent relationship between fisher linear discriminant analysis and karhunen - loeve expansion is revealed, i. e., ulda is essentially equivalent to one classical k - l expansion method. moreover, we enhance ulda using the idea of another k - l expansion method, and finally an optimal k - l expansion method is developed

    揭示了具有統計不相關性的線性鑒別分析與經典的k - l展開方法的內在關系，即不相關的線性鑒別分析方法與包含在類均值向量中判別信息的最優壓縮方法是等價的，並在此基礎上導出了一種最優k - l展開方法。
  10. Extended approach three : a hierarchical reduction approach is imported, which is based on the combination of statistical feature selection and linear discriminant analysis

    拓展演算法三：引入統計篩選和線性判別分析相結合的分層遞階約簡演算法。
  11. In this paper, we focus on two - class discriminating problem and chiefly study two types of linear discriminant analysis : principal component classifier ( pcc ) and fisher linear discriminant analysis ( flda )

    本文就兩分類問題，研究了兩種線性判別：主分量分類器和fisher判別分析。
  12. A face - recognition algorithm based on fisher linear discriminant analysis is studied in detail which combines principal component analysis ( pca ) based eigenface method and linear discriminant analysis ( lda ) method

    該方法將基於主成分分析（ pca ）的特徵臉方法和基於線性判別分析（ lda ）的分類方法有機的結合起來。
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