sampling errors

"sampling errors"的翻譯和解釋

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千萬(wàn)人都在用的超大詞匯詞典翻譯APP

例句與用法

Data mining , association rule , frequent itemset , sample error , multi - scaling sampling references 1 evfimievski a , srikant r , agrawal r , gehrke j . privacypreserving mining of association rules
已有的研究表明：根據(jù)數(shù)據(jù)庫(kù)的特性，動(dòng)態(tài)的選取樣本大小進(jìn)而獲取可接受的近似關(guān)聯(lián)規(guī)則的自適應(yīng)取樣方法是解決上述問(wèn)題的一種好方法。
By gender , it was 68 percent of the male respondents , and 66 percent of the female participants - a statistically insignificant difference given the study ' s margin of sampling error of plus or minus 2 percentage points
按性別比例來(lái)說(shuō)，分別有68的男性被調(diào)查者和66的女性被調(diào)查者是互聯(lián)網(wǎng)一組。而這個(gè)小小的差別并不具備統(tǒng)計(jì)學(xué)上的重要意義。
After using the result of the first time measure to correct the systematic error , but the second time sample error of measure has z - l , we can get bayes estimation of the systematic error : in the fifth chapter , we use the result of this paper in military practice
)后，若進(jìn)行第二次測(cè)量的樣本卻有z - l ，可求得此時(shí)系統(tǒng)誤差的的估計(jì)( ? )為：第五部分內(nèi)容給出的實(shí)例說(shuō)明了在軍事實(shí)踐和軍事仿真上，運(yùn)用本文的結(jié)論確實(shí)可以方便地得到較以往方法更為準(zhǔn)確的結(jié)果。
Sample error probably caused from data collection circuit of rf virtual frequency spectrum analyzer instrument was emphasized , and some kinds of selectable resolution scheme were presented , then these schemes of advantages and disadvantages were compared and summarized by the way of theory analyse , matlab simulation and practice validate and so on , at last , the optimum scheme of rf virtual frequency analyzer instrument was decided and adopted
重點(diǎn)對(duì)虛擬射頻頻譜分析儀中的數(shù)據(jù)采集電路可能造成的采樣誤差進(jìn)行了分析，提出了幾種可供選擇的解決方案，經(jīng)過(guò)理論分析、 matlab仿真和實(shí)際驗(yàn)證等手段，對(duì)這幾種方案的優(yōu)缺點(diǎn)進(jìn)行了比較和總結(jié)，并最終確定了其中最適合虛擬射頻頻譜分析儀的方案。
In order to improve generalization capability of feedforward neural networks , the convinced networks generalization domain should be guaranteed to be close to networks input domain as maximal intrinsic error of networks output and maximal samples error are reduced by increasing hidden neurons in number in the progress of networks learning , otherwise generalization capability of feedforward neural networks is likely to be decreased
為了提高網(wǎng)絡(luò)的泛化性能，從理論上分析指出，在網(wǎng)絡(luò)學(xué)習(xí)過(guò)程中通過(guò)增加隱含層神經(jīng)元來(lái)降低網(wǎng)絡(luò)最大固有誤差和最大樣本誤差的同時(shí)，要求確保網(wǎng)絡(luò)泛化定義域盡可能接近網(wǎng)絡(luò)輸入定義域，否則將有可能降低網(wǎng)絡(luò)的泛化性能。