嶺回歸 的英文怎麼說

中文拼音 [lǐnghuíguī]
嶺回歸 英文
ridge regression
  • 嶺 : 名1. （頂上有路可通行的山） mountain; ridge 2. （高大的山脈） mountain range 3. （專指大庾嶺等五嶺） the five ridges
  • 回 : 回構詞成分。
  • 歸 : Ⅰ動詞1 （返回） return; go back to 2 （還給; 歸還） return sth to; give back to 3 （趨向或集中於...
  1. First, strategy analyzing : through analyzing the inner and outer environment factors such as history, resources, competence and its strengths and weaknesses, we identified the firm ' s competitive advantages, core competence and long term goal. especially, we compare and arrange the order of civil listing cement enterprises in china through establishing a series of relevant indexes and fuzzy subsets method. we forecast the firm ' s next 5 years manufacture capability by recession analysis

    論文分析了企業的內外環境，歸納出企業優劣勢及企業發展的機會和方向，特別通過對企業歷史、資源、能力的分析，總結出企業的戰略目標及核心能力，通過建立相關指標體系及模糊聚類對水泥行業上市公司的競爭地位進行了比較分析與排序，通過二元回歸方法對秦嶺水泥的生產規模進行預測。
  2. The superiority of generalized ridge estimation of regression parameter in growth curve model under pitman closeness criterion

    準則下生長曲線模型回歸參數陣廣義嶺估計的優良性
  3. We analyzed the data by applying analysis of variance ( av ), multiple stepwise regression analysis ( msra ), canonical correlation analysis ( cca ) and so on. additionally, new developing statistical method, linear structural relations ( l1srel ), was employed to throw light on the substantial acting mechanism

    應用傳統的（協）方差分析、多元逐步回歸分析、主成分回歸分析、嶺回歸分析、判別分析和典型相關分析等統計方法對影響學習成績的因素進行分析，並採用新近發展的線性結構方程模型（ linearstructuralrelations ， lisrel ）分析影響學習成績的? ?各個因素並探討其影響機制。
  4. Ridge regression learning in esn for chaotic time series prediction

    嶺回歸學習演算法及混沌時間序列預測
  5. Based on multi - factor orthogonal designed field experiment, the ridge regression models of seed yield components and seed yield of the 6 grass species are founded through ridge regression analysis with big samples

    摘要採用多區組多因素正交試驗設計，通過大樣本嶺回歸分析求出6種禾本科牧草種子產量因子與產量的嶺回歸模型。
  6. Based on the least squares and biased estimation especially ridge estimation, a new estimation, that is, generalized ridge estimation is put forward through studies on restriction of the parameter. model ' s prediction being considered, comparison of superiority of optimal and classical predictions with respect to the ridge estimation is showed. regression diagnoses especially distance for principal components estimation is discussed

    論文基於最小二乘估計及有偏估計特別是嶺估計，對參數的約束條件做了進一步研究，並提出一種新型估計即廣義嶺型估計；對模型的點預測問題進行深入探索，得出一種基於嶺估計關于經典預測和最優預測的最優性判別條件；也對回歸診斷特別是基於主成分估計的距離進行了深入探討。
  7. It is important to apply influence analysis, computing of correlation - conefficient and charactrestic value of the design matrix and ridge regression to set models. the quality of model is developed

    數據分析：影響分析和設計矩陣的相關系數和特徵值計算及嶺回歸技術的應用對改進模型的品質起到了重要作用。
  8. Based on the practical characteristics of large span and duplex arch of xipuling tunnel, this paper expounds the workings of regression analysis and the analytical method of monitoring and surveying data in the construction of tunnels

    摘要基於西圃嶺隧道大跨度、雙連拱的實際特點，闡述了隧道施工過程中監控量測數據的回歸分析原理及分析方法。
  9. Ridge regression analysis is modified least - squares estimation. it can offer a stable forecasting when there is strong correlation between variables

    嶺回歸分析是一種修正的最小二乘估計法，當自變量系統中存在多重相關性時，它可以提供一個更為穩定的預測。
  10. Nonlinear ridge regression modeling method based on radial basis function and its simulation research

    基於徑向基函數的非線性嶺回歸方法及模擬研究
  11. Genetic algorithm optimization based nonlinear ridge regression modeling method and its application in soft measurement

    優化的非線性嶺回歸方法及其在軟測量中的應用
  12. The problem of multivariate regression is resolved by ridge regression analysis, as there are multiple collinearity among the independent variables

    摘要應用嶺回歸分析可以解決自變量之間存在復共線性時的回歸問題。
  13. This article gives a program of ridge regression with sas6. 11 version, and illustrate the method of the ridge regression by an example

    本文給出了在sas6 . 11及以上版本中實現嶺回歸分析的程序，用具體實例說明進行嶺回歸的方法。
  14. The main research contents include : 1 、 this paper constructs the mixed collaborative sale forecasting model based on cpfr via integrating time series forecasting, multivariate regression and ridge regression. in addition, the model takes sale information as explanation variable

    具體研究內容包括: 1 、將時間序列預測、多元回歸、嶺回歸相結合，並將銷售信息作為銷售量的解釋變量，構建了cpfr流程下的混合協同預測模型。
  15. The main results are as follows : according to the approximate multicollinearity of matrix, the third chapter constrains the regression coefficient and obtains generalized ridge estimation of the linear model ' s parameter under the ellipsoidal restriction

    主要結果如下：論文第三章從設計矩陣的多重共線性角度出發，考慮回歸系數的橢球約束，獲得了橢球約束下線性模型參數的一種新型估計- -廣義嶺型估計。
  16. However, there have still some unresolved problems : first, how to determine the number and size of the clusters automatically during the clustering process. second, how to utilize the " local " ridge regression method which including multiple regularization parameters in learning rbf network. third, those clusters in irregular form ca n ' t represented by radial basis function, thus we must find some other basis functions that can describe the irregular form

    但是仍然存在幾個問題尚待解決：首先，聚類時怎樣自動確定簇的個數和半徑；其次，如何利用含有多個正規化參數的局部嶺回歸方法進行rbf網路學習；第三，如果簇的形狀是不規則的，則它很難用徑向基函數來描述，因此需要研究其它能代表不規則形狀的簇的基函數。
  17. Define a radial basis function ( rbf ) at center of each cluster and learning a two - layer neural network which consists of these rbfs, simultaneously, for the purpose of avoiding over - fitting, we make use of ridge regression method, which adding a weight penalty term including a appropriate regularization parameter on the cost function and then lead to a more smooth function

    為每一個簇的中心定義相應的徑向基函數（ radialbasisfunction ， rbf ） ，再對這些徑向基函數構成的兩層神經網路進行訓練，同時，為了避免產生過度擬合現象，本文採用了嶺回歸技術，即在代價函數中加入一個包含適當正規化參數的權值懲罰項，從而保證網路輸出函數具有一定的平滑度。
  18. Ridge and principal correlation estimation of the regression parameters and its optimality

    回歸系數的嶺型主相關估計及其優良性
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