我的Q是 这个Q是什么意思！请帮我翻译！我想了半天，都不知道！_百度知道英语翻译我上了半天才上到一次,好不容易才能给你发条信息.我的网络出了严重的错误,我想两个礼拜才能修好,
若相失0343
I SPEND HALF DAY TO BE ONLINE ONCE,FINALLY I CAN SEND YOU A MESSAGE .THEIR IS A PROBLEM WITH MY NETWORK,I THINK IT‘LL TAKE 2WEEKS TO FIX IT ,OH GOD~
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i sign in only once and spend my half days.leave the message doesn't easier to you.my network had awful bad.i am
fix it least two weeks .well oh! my god
On me in half talent to, has been able to give you with greatdifficulty the spiral spring information. My network left the seriouserror, I thought two weeks can fix, day
扫描下载二维码英语翻译跟什么概念相对呢?我想了半天也没太明白……意思是说……不是职业的?Um...should be calligraphist.要是‘民间艺人’就没什么争议了，可是‘民间书法家’到底是个什么概念呢？我觉得folk似乎……不大合适还有其他的译法吗？
calligrapher
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民间书法家Folk Calligrapher参照：中国民间文艺家协会---China Society for the Study of Folk Literature
扫描下载二维码英语好的人,请翻译这个单词什么是意思?总听到一些年轻的人在说damn good ,或是在一些电影里也能听到这个词组.请问这是什么意思?在字典里我查到是：‘诅咒,好‘.我想知道它的真正含义是什么呀?谢谢了.
_童佳倩丶23
damn是可以用来形容一种极端的状况虽然damn可以说是一种粗口单用的时候就表示一种厌烦愤怒的情绪不过在美国生活中还是很常用damn good就是非常好的意思
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应该是damed good，说得时候没有ed,听起来就是dame good,意思是TMD好。此外，还经常会听到GOD DAMED之类（美国军队或球队教练），都是口头禅，是TMD的意思。
a damn nuisance一个十分讨厌的东西I don't carI don't give a damn what you do.我才不在乎你的所作所为呢。I don't carI don't give a damn what you do.我才不在乎你的所作所...
楼上的回答不错
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linear discriminant analysis是什么意思
中文翻译百科解释线性判别分析:&&&&adj. 1.线的，直线的。 2.长度的。 3.【数学】 ...:&&&&n. 【数学】判别式。 :&&&&n. (pl. -ses ) 1.分解，分析；【数学】解 ...详细百科解释
例句与用法Theory of fisher linear discriminant analysis and its application线性鉴别分析的理论研究及其应用A study on personal credit scoring using linear discriminant analysis线性判别式分析在个人信用评估中的应用A new two - dimensional linear discriminant analysis algorithm based on fuzzy set theory基于模糊集理论的二维线性鉴别分析新方法Extended approach three : a hierarchical reduction approach is imported , which is based on the combination of statistical feature selection and linear discriminant analysis拓展算法三：引入统计筛选和线性判别分析相结合的分层递阶约简算法。 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 )本文就两分类问题，研究了两种线性判别：主分量分类器和fisher判别分析。 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线性投影分析，包括主分量分析(或称k - l变换)和fisher线性鉴别分析，是特征抽取中最为经典和广泛使用的办法。 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该方法将基于主成分分析( pca )的特征脸方法和基于线性判别分析( lda )的分类方法有机的结合起来。 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揭示了具有统计不相关性的线性鉴别分析与经典的k - l展开方法的内在关系，即不相关的线性鉴别分析方法与包含在类均值向量中判别信息的最优压缩方法是等价的，并在此基础上导出了一种最优k - l展开方法。 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基于对模式识别系统的深入研究，提出了从响应阶段数据提取特征的方法，节省了测试所需时间；比较了主成分分析法( principalcomponentanalysis , pca )与线性判别式法( lineardiscriminantanalysis , lda )两种模式识别方法在实际应用中的不同结果，分析了原因；设计了采用误差反传算法back - propagationalgorithm , bpa )的前向人工神经网络( artificialneuralnetwork , ann ) ，并指出其应用中存在的问题，提出了改进建议；利用pca lda与ann相结合的方法改善了系统的识别性能。 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所提出的这两种方法的共同特点是，在进行图像特征抽取时，不需要事先将图像矩阵转化为高维的图像向量，而是直接利用图像矩阵本身构造图像散布矩阵，然后基于这些散布矩阵进行主分量分析与线性鉴别分析。 更多例句：&&1&&&&
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