例句與造句

1. Theory of fisher linear discriminant analysis and its application
   線性鑒別分析的理論研究及其應(yīng)用
2. A study on personal credit scoring using linear discriminant analysis
   線性判別式分析在個(gè)人信用評(píng)估中的應(yīng)用
3. A new two - dimensional linear discriminant analysis algorithm based on fuzzy set theory
   基于模糊集理論的二維線性鑒別分析新方法
4. 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(lèi)問(wèn)題，研究了兩種線性判別：主分量分類(lèi)器和fisher判別分析。
5. 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，是特征抽取中最為經(jīng)典和廣泛使用的辦法。
6. It's difficult to find linear discriminant analysis in a sentence. 用linear discriminant analysis造句挺難的
7. 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
   該方法將基于主成分分析( pca )的特征臉?lè)椒ê突诰€性判別分析( lda )的分類(lèi)方法有機(jī)的結(jié)合起來(lái)。
8. 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(tǒng)計(jì)不相關(guān)性的線性鑒別分析與經(jīng)典的k - l展開(kāi)方法的內(nèi)在關(guān)系，即不相關(guān)的線性鑒別分析方法與包含在類(lèi)均值向量中判別信息的最優(yōu)壓縮方法是等價(jià)的，并在此基礎(chǔ)上導(dǎo)出了一種最優(yōu)k - l展開(kāi)方法。
9. 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
   基于對(duì)模式識(shí)別系統(tǒng)的深入研究，提出了從響應(yīng)階段數(shù)據(jù)提取特征的方法，節(jié)省了測(cè)試所需時(shí)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(shí)別方法在實(shí)際應(yīng)用中的不同結(jié)果，分析了原因；設(shè)計(jì)了采用誤差反傳算法back - propagationalgorithm , bpa )的前向人工神經(jīng)網(wǎng)絡(luò)( artificialneuralnetwork , ann ) ，并指出其應(yīng)用中存在的問(wèn)題，提出了改進(jìn)建議；利用pca lda與ann相結(jié)合的方法改善了系統(tǒng)的識(shí)別性能。
10. 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(diǎn)是，在進(jìn)行圖像特征抽取時(shí)，不需要事先將圖像矩陣轉(zhuǎn)化為高維的圖像向量，而是直接利用圖像矩陣本身構(gòu)造圖像散布矩陣，然后基于這些散布矩陣進(jìn)行主分量分析與線性鑒別分析。
11. 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(biāo)準(zhǔn)人臉庫(kù)和nust603人臉庫(kù)上的試驗(yàn)結(jié)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(shù)將特征抽取的速度提高了一個(gè)數(shù)量級(jí)以上。不僅如此，其識(shí)別精度依然高于傳統(tǒng)的eigenfaces與fisherfaces方法。
12. 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(tǒng)計(jì)不相關(guān)性的線性鑒別分析的理論構(gòu)架，給出了求解不相關(guān)的最優(yōu)鑒別矢量集的一個(gè)非常簡(jiǎn)單而有效的算法，并指出統(tǒng)計(jì)不相關(guān)的線性鑒別分析的理論是經(jīng)典的fisher線性鑒別法的進(jìn)一步發(fā)展。