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the cross validation
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  交叉有效性
     The thesis discusses the basic principle,the basic modeling method and the cross validation of Partial Least Squares regression,which is applied in the related analysis of hydrology and is compared with the Least Squares regression.
     介绍了偏最小二乘回归的基本原理、建模基本思路和交叉有效性判别方法,将偏最小二乘回归模型应用于水文相关分析,并与最小二乘回归模型进行比较分析。
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     And then, based on the measurement foundation described above and the partial least square regression method, this paper established the PCSI evaluation and the predictive model, and derived the PCSI regression equation and formula of the factor weightαj of product price and performance and so on, the cross validation as well as the PCSI analysis computation diagram. All of these had laid the foundation for the PCSI analysis and application.
     再次,建立了基于偏最小二乘回归方法的PCSI评价模型,导出PCSI回归方程,产品性价要素权重αj等主要PCSI分析参数的计算公式,交叉有效性判据以及PCSI分析计算框图,为PCSI分析和应用奠定了基础;
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     The thesis discusses the basic principle. the basic modeling method. and the cross validation of Partial Least Square (PLS) regression.
     介绍了偏最小二乘回归的基本原理、建模基本思路和交叉有效性判别方法,编制了偏最小二乘回归程序;
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     The basic principle ,the basic modeling method and the cross validation of PLS regression are introduced and the application of simple PLSs regression to hyperbolic decline curve is discussd .
     介绍了偏最小二乘法的基本原理、建模基本思想和交叉有效性判别法并讨论了该方法在双曲递减方程中的应用.
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     This method combines the forecasting by means of model with non-model style data connotation analysis,thus,the establishment of model by mean of regression,simplification of data structure and the analysis of correlative uncertainty of model factors can be carried out simultaneously. The accuracy of the dam safety monitoring model is controlled by the cross validation test.
     引进偏最小二乘法,对大坝安全监测变量及其影响因子进行偏最小二乘回归分析,将建模预测分析方法与非模型式的数据内涵分析有机结合,可同时实现回归建模、数据结构简化以及因子相关的不确定性分析,所建立的大坝安全监控模型,其精度可通过交叉有效性检验来控制。
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  “the cross validation”译为未确定词的双语例句
     Results The cross validation R2CV=0.660,non-cross validation R2=0.976,standard error of estimate SEE=0.196,F=181.916.The activities of the four compounds were predicted by this mode and the results were compatible to actual values.
     结果该模型的交叉验证相关系数R2CV=0.660,非交叉验证相关系数R2=0.976,标准偏差SEE=0.196,F=181.916。 用此模型预测了4个SSRIs的活性,结果与试验值相符。
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     1. In the first chapter we give a brief description of semiparametric regression models, the Cross Validation and the Generalized Cross Validation of choosing the smoothing parameter. Generalized Cross Validation is used in this paper.
     1.第一章首先简单地介绍了半参数回归模型和选择光滑参数的交叉核实法(Cross Validation,简称CV)以及广义交叉核实法(Generalized Cross Validation,简称GCV),本论文均采用GCV法选择光滑参数。
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     The cross validation method withleave-one-out was used to estimate the recognition rate, and the accuracy was 94.44%.
     利用这些参数组成训练样本设计识别器,并用“留一法”估算该识别器的识别正确率为 94.44%。
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     We select 7 vectors to build the model through the method of stepwise multiple regression (SMR),the correlation coefficient of which is 0.957.Then the model is evaluated by performing the cross validation with the leave-one-out (LOO) procedure to give R_(CV)=0.933.So we can get the conclusion that the stability and predictability of the model is good.
     用逐步回归的方法筛选7个变量进行建模,相关系数R=0 957,再用留一法(Leave one out,LOO)交互检验对模型进行评价,得到的相关系数RCV=0 933。 所以此模型具有较好的稳定性和预测能力。
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     The significant model shows a higher ability to explain and predict the activity of COX II inhibitors, with the cross validation R CV 2=0 718, non cross validation R 2=0 992, F=260 624, and SEE (standard error of estimate)=0 072. Three compounds were selected as a predicting set, the low deviations of calculated values from the measured ones suggesting a powerful predictive ability of the model.
     该模型的交叉验证系数RCV2 =0 718; 传统相关系数R2 =0 992 ,F =2 6 0 6 2 4,标准偏差S =0 0 72。
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     Cross Infection
     交叉感染
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     and maize cross.
     与玉米杂交导入一个小麦加倍单倍体 (DH)植株中。
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     Cross The River
     Cross The River过河
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     It is the "gold-cross" of the Earth.
     这是地球生物多样性的“黄金十字带”.
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     The validation to test of T.
     文章采用大型通用有限元程序ANSYS,对T.
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  the cross validation
The cross validation method had to be used due to the scarcity of the data.
      
Spherical semivariogram model of each element for the pooled data set of S10+5 passed the cross validation tests.
      
In this article, we propose a bootstrap method to find the optimal window width in print-tip normalization by applying the cross validation technique.
      
The cross validation of experimental and numerical data allows significant conclusions to be reached on conjugate conduction and convection with buoyancy effects in horizontal duct flows.
      
In this paper, we present two ways to mix kernel matrices, where the mixing weights are optimized to minimize the cross validation error.
      
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Given that Yi = f(ti) +2i ,i = 1,2,…, n, Let fu (t; λ*) be the kernel estimates of the regression function f(t)and λ* be the cross-validation choice of bandwidth based on the average squared prediction error. Under weaker moments conditions that Eε2i≤∞. we studied the strong consistency and the asymptotic optimality of fn(t; λ*)in terms of the average squared error.

给定Y_i=f(t_i)+ε_i,i=1,2,…,n,令f_n(t_jλ~*)是回归函数f(t)的核估计并且λ~*是窗宽基于均方预测误差的Cross—Validation选择.在较弱的矩的条件E_(ε_i)~2<∞下,我们研究了f_n(t_iλ~*)的藉助于均方误差的强相合性以及渐近最优性.

Determining the expression of drift and the variogram of residuals is the crux of Kriging valuation, and it is of great importance to analyse the spatial distribution characteristics of the regionalized variables. In this paper, the advantages and disadvantages associated with the cross validation method and the iterative method for determining the expression of drift and the variogram of residuals are discussed. It points out that an organic combination of the two methods will gain the effect of keeping...

Determining the expression of drift and the variogram of residuals is the crux of Kriging valuation, and it is of great importance to analyse the spatial distribution characteristics of the regionalized variables. In this paper, the advantages and disadvantages associated with the cross validation method and the iterative method for determining the expression of drift and the variogram of residuals are discussed. It points out that an organic combination of the two methods will gain the effect of keeping the strong points and making up deficiencies. In order to speed up the cross validation and mprove the accuracy of check, cycled cross validation method is proposed. Application studies have been made by using the data from the oilfields.

在进行泛克里金估值时,确定漂移的形式及剩余的变异函数是一个关键的问题。本文分析了确定剩余的变异函数和漂移开式的交叉验证法和迭代法的优缺点,指出了将这两种方法的有机的结合起来会获得取长补短的效果,同时提出了循环交叉验证的方法,以加快交叉验证的速度,提高检验精度,并利用油田实际数据进行了应用的研究。

In this paper, neural networks pattern recognition was applied to discri(?)inate the formation conditions of ternary chinse d88 structure of internal metal compound(?). The neural network was constituted of three layers and the back propagation algorithm was used. To evaluate the performance of the obtained model the cross-validation strategy was employed. The results showed that 71 compounds could be classified satisfactorily by neu(?)d networks when the bond parameters were used as the input, and...

In this paper, neural networks pattern recognition was applied to discri(?)inate the formation conditions of ternary chinse d88 structure of internal metal compound(?). The neural network was constituted of three layers and the back propagation algorithm was used. To evaluate the performance of the obtained model the cross-validation strategy was employed. The results showed that 71 compounds could be classified satisfactorily by neu(?)d networks when the bond parameters were used as the input, and there was an optimal number of hidden layer nodes where the percent of correct classification is higher while the calculi tion time used is fewer.

本文将神经网络模式识别用于金属间化合物3元填隙d88结构形成条件的判别;神经网络由3层组成,训练用后向传播算法。为了评价所得模型的行为,使用了交叉验证法。计算结果表明,当适当的键参数作为输入时,71种化合物能被正确地分类,且隐层节点数经优选后可以提高分类的准确率,减少计算时间。

 
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