例句與造句

1. Unit weight approach for model sediment
   模型試驗(yàn)的泥沙重度淺析
2. Novel weight approach for interval numbers comparison matrix in the analytic hierarchy process
   中判斷矩陣的區(qū)間權(quán)重及其一致性檢驗(yàn)
3. Sci . , 1996 , 2 : 637 - 649 . 15 salton g , buckley c . term - weighting approaches in automatic retrieval
   Clascn方法可以和其他任何精確top - k算法相結(jié)合形成一個(gè)新的高效近似top - k算法。
4. After simulating the two classic algorithms proposed by rui and aksoy , we propose a novel feature re - weighting approach for relevance feedback
   在此基礎(chǔ)上,提出了一種新的基于修改特征權(quán)重的相關(guān)反饋新算法。
5. This resulted in the emergence of a broad consensus on a weighted approach to the measurement of risk , on and off the balance sheet
   也就是說(shuō)，達(dá)成交易所依據(jù)的條款，同兩個(gè)獨(dú)立主體在平等條件下所達(dá)成的交易依據(jù)的條款是同樣的。
6. It's difficult to find weighted approach in a sentence. 用weighted approach造句挺難的
7. On the basis of studying feature re - weighting approach for relevance feedback in cbir , we propose the algorithm of combining the lower - feature and semantic to retrieve images
   本文在研究基于修改特征權(quán)重的相關(guān)反饋算法基礎(chǔ)上,提出了綜合低層特征和語(yǔ)義特征進(jìn)行圖像檢索的算法。
8. In this dissertation , we analyze the content - based image retrieval technology and several algorithms of the relevance feedback in cbir . our emphasis is the feature re - weighting approach for relevance feedback in cbir
   本文全面分析了圖像檢索各種技術(shù)和圖像檢索技術(shù)中的相關(guān)反饋算法,對(duì)基于修改特征權(quán)重的相關(guān)反饋算法進(jìn)行了重點(diǎn)分析,對(duì)經(jīng)典算法進(jìn)行了仿真實(shí)驗(yàn)。
9. Exemplified with a number of representative systems which are highly valuable in industry and academically important , including electrode membranes of fuel cells , sesame seeds as well as hard bones and cartilages of marine fish , the extensive applications of microscopic mri images with various weighting approaches to the investigations of micro - structures and dynamics including the developmental processes of plants are demonstrated
   以具有高度產(chǎn)業(yè)價(jià)值及重要學(xué)術(shù)意義的幾個(gè)典型體系：燃料電池電極月莫、芝麻種子和海生魚(yú)骨為例，說(shuō)明各類(lèi)加權(quán)圖象在微結(jié)構(gòu)和動(dòng)態(tài)（包括植物生長(zhǎng)過(guò)程）研究中的廣泛應(yīng)用。
10. When taking part in the bci competition iii , t - weighted approach for feature extraction and reinforcement learning of classifier design are proposed . compared to other methods , t - weight approach has the advantages of requiring less a prior knowledge , exploring more information and computing faster . reinforcement learning is an optimization method both model driven and data driven aiming at mining the discriminative information as more as possible , and improving both the fitting and generalization ability of an existing classifier
    相比其它特征提取方法, t加權(quán)方法具有對(duì)先驗(yàn)知識(shí)要求少、信息利用充分、計(jì)算快速等優(yōu)點(diǎn);而分類(lèi)器設(shè)計(jì)的強(qiáng)化學(xué)習(xí)方法是模型驅(qū)動(dòng)與數(shù)據(jù)驅(qū)動(dòng)相結(jié)合的一種分類(lèi)器優(yōu)化方法,其思想在于充分挖掘樣本判別信息,在已有分類(lèi)器基礎(chǔ)上進(jìn)一步提高對(duì)數(shù)據(jù)的擬合能力及泛化能力。