稀疏數據問題 的英文怎麼說

中文拼音 [xīshūshǔjùwèntí]
稀疏數據問題 英文
sparse data problem
  • 稀 : Ⅰ形容詞1 （事物出現得少） rare; scarce; uncommon 2 （事物之間距離遠; 空隙大） sparse; scattered 3...
  • 疏 : Ⅰ動詞1 （疏通） dredge （a river etc ）2 （疏忽） neglect 3 （分散; 使從密變稀） disperse; scatte...
  • 數 : 數副詞（屢次） frequently; repeatedly
  • 據 : 據Ⅰ動詞1 （占據） occupy; seize 2 （憑借; 依靠） rely on; depend on Ⅱ介詞（按照; 依據） according...
  • 問 : Ⅰ動詞1 （請人解答） ask; inquire 2 （詢問; 慰問） question; ask about [after]; inquire about [aft...
  • 題 : Ⅰ名詞1. （題目） subject; title; topic; problem 2. （姓氏） a surname Ⅱ動詞（寫上） inscribe; write
  • 稀疏 : few and scattered; few and far between; thin; sparse
  • 數據 : data; record; information
  • 問題 : 1 （需回答的題目） question; problem 2 （需研究解決的矛盾等） problem; matter 3 （事故或意外） tr...
  1. The method based on statistics has the problem of training data ' s rarefaction, and what restricts the more progress of corpus is the too large workload of manual tagging

    基於統計的漢語自動分詞方法存在訓練數據稀疏的問題，而人工標注工作量過大又制約著語料庫規模的進一步擴大。
  2. This problem arises from the circuit layout of vlsi designs, interconnection networks, sparse matrix computations, error - correcting code designs, data structures, biology, etc, which has extensive backgrounds

    圖的嵌入問題是從稀疏矩陣的計算、數據結構、 vlsi電子線路設計和分子生物學等問題中提取出來的數學模型，有著廣泛的應用背景。
  3. This paper present the classic backtracking as an example, through comparing, explains backtracking efficiency difference under various data structure ; when database can be expressed in sparseness matrix, then it can be expressed in 4 - way linked list, which improves greatly the efficiency than before

    以一個典型的回溯問題為例，通過對比，說明回溯法在不同數據結構下，其時間效率的差異，驗證對于可表示成稀疏矩陣的數據集，在使用四向鏈表結構時，可以大大提高時間效率。
  4. This paper applies generalized multipler method to translate convex quadratic programs with equal constraints and non - negative constraints into simple convex quadratic programs with non - negative constraints. the new algorithm is gotten by solving the simple quadratic program. it avoids the computation of inverse matrix and exploits sparsity structure in the matrix of the quadratic form. the results of numerical experiments show the effectiveness of the algorithm on large scale problems

    根據廣義乘子法的思想，將具有等式約束和非負約束的凸二次規劃問題轉化為只有非負約束的簡單凸二次規劃，通過解簡單凸二次規劃來得到解等式約束和非負約束的凸二次規劃新演算法，新演算法不用求逆矩陣，這樣可充分保持矩陣的稀疏性，用來解大規模稀疏問題.數值結果表明：在微機486 / 33上就能解較大規模的凸二次規劃
  5. The key issue of the study is to solve the effective exhaustion item type of the small - scale original numerator and sparse data set, so it can make analysis on all the possibilities of the containing type

    研究的核心問題是解決對小規模原子項和稀疏數據集進行有效的窮舉項類型，從而進行所有蘊涵式可能性的分析。
  6. It adopts the hierachical clustering in vocabulary vsm model because of its special function, on the other hand enriches the subcategory tagging information by rules, it can decrease me data sparse problem, and introduces the confidence intervals into the model for the selection of priority between statistics and rules

    另外還對標注模型從兩方面作了優化，由於詞匯特徵向量的特殊作用，本文對特徵詞匯採用層次聚類來提高其分類精度；另一方面，引入規則來進一步豐富細分類標注信息，減少數據稀疏等問題，並且引入置信度來選擇統計與規則的優先關系。
  7. We look at the problem of learning from examples as the problem of multivariate function approximation from sparse chosen data, and then consider the case in which the data are drawn, instead of chosen, according to a probability measure

    並檢視稀疏精選值中多變量函數近似法等這些從實例學習法所發現的問題，然後根據機率衡量，審思隨機獲得資料而非選定資料的案例。
  8. In the first and second section of chapter 1 we introduce the model of dimension reduction problem, put forward the concepts of dimension - reduction function and embedding function, and make a classification for the dimension reduction problem ; in section 1. 3 we discuss " the curse of dimension " and the sparsity of high - dimensional space ; in section 1. 4 we discuss " intrinsic dimension " and its estimation based on the model of dimension reduction

    第一章首先提出了降維的模型和定義，討論了相關的問題；第三節討論「維數禍根」現象和高維空間的稀疏性，通過實例分析其對高維空間的數據分佈特性具體影響；第四節討論了本徵維數及其估計的基本問題。
  9. The relative frequency training ( rft ) method is used to estimate the model parameters. and the problem of the data sparseness is solved through the backing off data smoothing algorithm

    同時採用了回退式參數平滑演算法來解決了一階隱馬爾可夫模型的數據稀疏問題。
  10. There are several problems in traditional systems from the current b2c website electronic commerce personalization recommendation system : data sparsity, the commodities which are purchased or rated by users only occupy the total commodity number about 1 % ; new project problem, the new user and the new commodity which doesn ’ t be purchased or rated can ’ t be recommended ; solely recommend means, long data processing and low recommendation precision

    本文通過對當前b2c網站的電子商務個性化推薦系統分析，發現傳統的推薦系統有如下問題:數據稀疏性問題，用戶購買或評分的只佔總商品數的1 %左右;新項目問題，對于未被購買或評分的新商品、新用戶一般不能進行推薦;推薦方法單一、數據處理耗時過長以及推薦精度不高的問題。
分享友人
分享到 Line

分享到臉書