樣本分數 的英文怎麼說

中文拼音 [yàngběnfēnshǔ]
樣本分數 英文
sampling fraction
  • 樣 : Ⅰ名詞1. （形狀） appearance; shape 2. （樣品） sample; model; pattern Ⅱ量詞（表示事物的種類） kind; type
  • 本 : i 名詞1 （草木的莖或根）stem or root of plants 2 （事物的根源）foundation; origin; basis 3 （本錢...
  • 分 : 分Ⅰ名詞1. （成分） component 2. （職責和權利的限度） what is within one's duty or rights Ⅱ同 「份」Ⅲ動詞[書面語] （料想） judge
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 樣本 : sample book; specimen; advanced copy; sample; muster; scantling; instance; statistics
  1. A proportion of the samples are sent to the epds environmental microbiology laboratory where they receive same - day analyses for e. coli, faecal coliforms, and 5 - day biochemical oxygen demand. the remainder of the samples are stored in a cold room before being delivered to the government laboratory, where they are analysed according to the 39 other parameters outlined in chapter 2

    部分樣本會送往署內的環境微生物學化驗室即日進行大腸桿菌糞大腸菌群和五天生化需氧量分析，其餘樣本則先存放于冷凍室，稍後送交政府化驗所，根據第二章所述進行39項其他參數的分析。
  2. A proportion of the adjacent samples " euclidian distance based analytical method is presented in this paper. the result of the analysis can direct user to select the test samples and test the effect of the selection

    本文提出「相鄰樣本距離比例關系分析法」 ，該方法通過分析距離比例關系變化對樣本數據產生一定認識，該認識又指導使用者進行樣本分配以及檢驗樣本分配效果。
  3. Pcc takes the normal vector of a hyperplane as the projecting direction, onto which the algebraic sum of all samples " projections is maximized, such that samples in one class can be separated well from the other by this hyperplane

    主分量分類器是在兩類樣本投影代數和最大的前提下，獲得最佳投影方向（分類面法方向） ，實現樣本分類。它的不足之處在於： 1
  4. One class classification is a machine learning approach different from the traditional pattern recognition approach where two or more class samples are required. however in some real - life cases, we can hardly, even not, get the samples of some classes, or have to pay costly price to obtain the so - needed samples, such as in the case of machinery malfunction. and while in other cases, the sizes of samples among classes are imbalance, such as medical diagnosis

    單類分類器是不同於傳統模式識別的一種機器學習方法，傳統模式識別方法一般需要多個類別的樣本（至少兩個） ，而在有些場合中，幾乎無法獲取多類的樣本，或者獲取其樣本所需花費的代價非常高，比如：機器故障中我們不可能為了去獲得故障樣本而讓機器特意產生故障；又有些場合的類別樣本個數嚴重不平衡，比如醫學上的疾病特徵與非疾病特徵的比例是嚴重不平衡的。
  5. All of the data were analyzed for variance analysis and newman - keuls test between groups using software stat. results : 1

    所得數據用stat軟體進行方差分析和各樣本均數間的newman - keuls檢驗。
  6. In order to verify the feasibility of ann, adopting same training sample the author establishes quadratic curve model and index model of tourism foreign exchange income and cubic curve model and index model of total inbound tourist quantity

    為了驗證人工神經網路模型的可行性，筆者用同樣的訓練樣本分別建立了旅遊外匯收入二次曲線模型、指數曲線模型和入境遊客三次曲線模型、指數曲線模型。
  7. By means of trigonometrical progression method and the mainline track spectrum, the sample function of the chinese mainline railway track random geometric irregularity is simulated. with the data obtained from track geometry inspection car on qinhuangdao - shenyang special line for passenger transport and arma time series model, the sample function of high - speed railway track random geometric irregularity are simulated. based on existing literature, the artificial bogie crawl waves at various different speeds are randomly simulated

    根據我國干線鐵路軌道譜，採用三角級數法模擬出干線鐵路和準高速鐵路軌道不平順的樣本函數；根據秦沈客運專線高速試驗段軌檢車資料，採用arma時間序列模型模擬了高速鐵路軌道不平順隨機樣本函數；在既有研究資料的基礎上模擬出各種速度客車構架人工蛇行波；用隨機變量描述道床橫向剛度，並進行了隨機模擬；將振動理論和穩定理論結合建立系統的分析模型和運動方程；根據monte ? carlo法編制了車輛?軌道耦合系統隨機振動分析程序，進行了無縫線路隨機動力響應分析，通過試驗對計算模型、計算方法進行了驗證。
  8. A novel approach was introduced to process and analyze the data sets for protein secondary structure prediction based on database technologies via constructing a database of the prediction data sets

    摘要將數據庫技術應用到蛋白質二級結構預測的樣本集處理和分析上，建立了二級結構預測樣本集數據庫。
  9. This paper uses the panel data from listed companies in china ' s three typical industries from year 1998 to 2004 to empirically test whether industrial characteristic differentials can significantly affect the correlation between ownership structure and corporate performance

    摘要選取三個具有顯著行業差異的代表性行業上市公司1998 ~ 2004年面板數據作為樣本，分析行業特徵差異對股權結構與公司績效相關性的影響。
  10. Normal behavior and anomaly are distinguished on the basis of observed datum such as network flows and audit records of host. when a training sample set is unlabelled and unbalanced, attack detection is treated as outlier detection or density estimation of samples and one - class svm of hypersphere can be utilized to solve it. when a training sample set is labelled and unbalanced so that the class with small size will reach a much high error rate of classification, a weighted svm algorithm, i

    針對訓練樣本是未標定的不均衡數據集的情況，把攻擊檢測問題視為一個孤立點發現或樣本密度估計問題，採用了超球面上的one - classsvm演算法來處理這類問題;針對有標定的不均衡數據集對于數目較少的那類樣本分類錯誤率較高的情況，引入了加權svm演算法-雙v - svm演算法來進行異常檢測;進一步，基於1998darpa入侵檢測評估數據源，把兩分類svm演算法推廣至多分類svm演算法，並做了多分類svm演算法性能比較實驗。
  11. Based on these descriptions, a nd model called support vector data description ( svdd ) is founded. ( 2 ) a qualitative guide for setting those parameters in oc - svms is investigated. a multi - layer high - speed training strategy was proposed to enable support vector algorithm to handle large training data

    （ 2 ）通過分析支持向量機中模型參數對檢測結果的影響，給出了確定這些參數的一般方法;提出了一種分層式的快速訓練方法，克服了樣本個數和維數對支持向量演算法應用的制約，提高了訓練效率。
  12. To deal with the two - class classification problem, a new strategy of integrating the genetic algorithm and the lvq artificial networks is adopted to reduce the dimensions in high dimension space. using this method, the classification accuracy is 100 % to leukemia dataset, and 91. 27 % to clone cancer dataset

    把遺傳演算法和lvq神經網路結合進行高維空間的特徵選擇，以解決兩類別的樣本分類問題，並利用白血病和大腸癌基因晶元數據進行了實例計算，分別達到了100和91 . 27的準確度。
  13. Figures for the weighted sample were obtained from weighting the figures of the raw sample by the gender - age distribution of the hong kong population, according to the 2001 population census results published by the hksar government

    被訪者基本個人資料加權樣本的數據，是根據政府公布的2001年人口普查結果，把原始數據按照年性別分佈情況加權調整后的結果。
  14. In samcluster system, the following cluster methods including hierarchical cluster analysis. k - means, and self - organizing map ( som ) and the feature selection methods based on coefficient of variation ( cv ) and simple t - test were integrated. to evaluate the performance of the samcluster system, the samcluster was applied to four expression datasets colon, leukemia72

    在samcluster系統中，整合了下列聚類演算法：譜系聚類、 k -平均值聚類和自組圖聚類與變異系數計算和t -檢驗等基因變量選擇方法，並提出了一致的樣本分型概念，通過對四個基因表達譜的數據集colon 、 leukemia72 、 leukemia38和ovarian的測試，結果表明：誤判的樣本數分別為5 、 1 、 0和0個，因此，基因水平的樣本分型與樣本的臨床分型高度一致。
  15. The unit includes topics such as basic probability and random variables, data summarization and display, data quality, probability models for data including the normal, poisson, binomial and sampling distributions and their important properties

    本課程課程包括基本概率和隨機變量、數據摘要和顯示、數據質量、數據概率模型（常規、非常規、二項式和樣本分類和它的重要屬性。
  16. In second chapter, some limit theorems for function of countable markov chains in markovian environments were obtained, at the same time, some sufficient conditions on the jointly markov chains and sample function of the jointly markov chains were given

    在第二章中，建立了具有離散參數的馬氏環境中馬氏鏈函數的極限定理，並給出了加在雙鏈和過程樣本函數上的一些充分條件。
  17. 2. administrating : selects 180 cases from six different types of enterprises, makes statistical and logical analysis about the cases by using spss10. 0 software

    2 、正式施測：在六個不同性質的企業取樣，樣本總數為180例，利用spss10 . 0軟體包進行統計分析和邏輯分析。
  18. The results of simulation show that the soft sensor based on the proposed method has high precision and is suitable for time - varying system with samples which distribution is not uniform. 5

    工業數據模擬結果表浙江大學博士學位論文明，該方法在線建立的軟測量模型精度高，很適合慢時變對象、且訓練樣本分佈不均勻情況下的軟測量建模。
  19. Due to large - scale and imbalanced churn data, an improved svm - imbalance core vector machine svm ( icsvm ) was presented to predict customer churn, which has better arithmetic performance than others based on the test of real telecom data set

    基於實際客戶流失數據樣本數據量大、正負樣本分佈不平衡的特點，提出了一種改進支持向量機演算法，並將其用於電信行業的客戶流失預測。
  20. Nerve network have the ability to automatic orgnanization and automatic studying, and can adapt to find the rule, which is concealed in the sample data. the studying ability of the nerve network is different form the traditional pattern recognition, the latter depend on the knowledge about the ruler of the recognition, while it is not necessary to know the knowledge about the ruler of recognition for the nerve network, which can get the relation of samples from the data. the main job of this paper is about how to apply nerve network to the real time recognition

    神經網路具有自組織和自學習能力，能夠在學習過程中，自適應地發現蘊涵在樣本數據中的內在的特性及規律性，這一自學習的能力與傳統模式識別中所採用的方法不相同，後者往往依賴于編程者對識別規則的先驗知識，而神經網路對所要處理的對象在樣本空間的分佈狀態無需作任何假設，而是直接從數據中學習樣本之間的關系，因而它們還可以解決那些因為不知道樣本分佈而無法解決的識別問題。
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