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small data set
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  “small data set”译为未确定词的双语例句
     The changing processes and the Lyapunov exponents is calculated by the small data set method,the result shows that the chaos exactly exist in the traffic flow of OVM.
     通过分析这种车头间距变化过程的变化曲线并利用小数据量方法计算Lyapunov指数,证明了基于OVM模型产生的交通流中存在着混沌现象。
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     2. An information entropy based Reduct searching algorithm is proposed to benefit the applications of rough sets theory when it is used to small data set.
     2.提出了基于粗集动态建立规则模型的方法,克服了小数据库建模的困难和粗集约简的缺点。
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     Objective The method of three-dimension reconstruction with small data set was presented and then was used to investigate location and shape of the brain nucleus.
     目的 探索小数据量条件下三维重建方法,并用之研究脑内神经核团的空间形态和位置.
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     Numerical test results show that SVR has good ability of modeling nonstationary financial time series and good generalization under small data set available.
     数值实验表明,SVR方法对非平稳的金融时间序列具有良好的建模和泛化能力。
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     The classical algorithm for classification is very effective for small data set,but it loses the ability of classification for large data set sometimes.
     经典的分类算法对小型数据集分类是非常有效的,但当面对超大型数据集时往往失去了分类能力。
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  相似匹配句对
     Preprocessing of small sampling data
     小样本采样数据的预处理
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     Small.
     Small、为正品。
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     DATA
     数据
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     On the contrary, small sample data are at large.
     与之相反,小样本数据和信息随处可见。
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     data sections.
     data块的DES加密过程.
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  small data set
The way that skill varies with season and the Southern Oscillation Index is consistent with other research but not statistically significant for this small data set.
      
This data set was then compared with the small data set presented by a company for benchmarking.
      
The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses.
      
Following this string of ideas leads to the discovery that the Bergman kernel can be "zipped" down to a strikingly small data set.
      
As an application fordemonstration, a small data set from a garigue community on ultramafic soils ofTuscany (central Italy) is used.
      
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A comparison of two methods for estimation of variance component,MIVQUE and KEML,was carried out on four data sets with Monte Carlo simulation. The model used was the sire model containing herd-year-season(HYS) effect(fixed),sire group effect(fixed)and sire effect(random), which is widely used in dairy cattle breeding. The largest data set consisted of 12847 records with 47 sires and 778 HYSs, which is corresponding to the milk yield data available currently in Beijing area. The smallest...

A comparison of two methods for estimation of variance component,MIVQUE and KEML,was carried out on four data sets with Monte Carlo simulation. The model used was the sire model containing herd-year-season(HYS) effect(fixed),sire group effect(fixed)and sire effect(random), which is widely used in dairy cattle breeding. The largest data set consisted of 12847 records with 47 sires and 778 HYSs, which is corresponding to the milk yield data available currently in Beijing area. The smallest data set comprised 200 records with 148 HYSs and 20 sires. The criterion for the comparison were the bias and variance, either theoretical or empirical based on 1000 repeated simulations, of the estimates. The results show that for the larger data sets the two methods are little different from each other:Bias<1% of the true values,correlation≈1,and variance(MIVQUE)≈variance(REML).For the smaller data sets the MIVQUE was significantly better than REM L.It was also shown that for REML the sample size like the first data set can satisfy its large sample properties of asymptotical unbiasedness and efficiency.

利用MonteCarlo方法,对4种数据结构进行了MIVQUE和REML两种方差组分估计方法的模拟比较。方差组分估计所用的模型为奶牛育种中常用的公畜模型,它包括场年季固定效应、公牛组固定效应和公牛随机效应。4种数据结构中最大的有12847个观察值,场年季效应和公牛效应水平数分别为778和47,它与北京市目前可利用的奶牛头胎产奶量记录资料相当。最小的数据结构只有200个观察值,148个场年季和20头公牛。比较指标为估计值的偏差和方差(理论的或根据1000次重复模拟所得的经验值)。结果表明,对于较大样本的数据结构,两种方法差异很小,它们间的估计值的相关接近于1,偏差小于真值的1%,方差近似相等。对于较小样本的数据结构,MIVQUE则明显优于REML。本研究还表明,对于REML来说,类似数据结构1的样本已能满足其渐近无偏性和有效性的大样本特性。

Objective The method of three-dimension reconstruction with small data set was presented and then was used to investigate location and shape of the brain nucleus.Methods Twenty adult whole brains from cadavers without gross pathologic changes were collected and were cut into a series of slices of 2 mm in thickness. The red nucleus, lentiform nucleus were reconstructed with the method based on surface modeling method. The volume and location of these reconstructed nuclei were measured by programs on computer....

Objective The method of three-dimension reconstruction with small data set was presented and then was used to investigate location and shape of the brain nucleus.Methods Twenty adult whole brains from cadavers without gross pathologic changes were collected and were cut into a series of slices of 2 mm in thickness. The red nucleus, lentiform nucleus were reconstructed with the method based on surface modeling method. The volume and location of these reconstructed nuclei were measured by programs on computer. Results The brain nuclei were reconstructed and data about shape and location of reconstructed brain nucleus was acquired. Conclusion The three dimension reconstruction method presented by this paper is effective to reconstruct brain nuclei and is helpful to identify precisely shape and location of brain nuclei.

目的 探索小数据量条件下三维重建方法,并用之研究脑内神经核团的空间形态和位置.方法 以20只整脑2mm厚的连续切片为数据源,运用曲面造型法,重建豆状核、红核等脑内神经核团的三维形状.结果 在微机上重建出脑内神经核团的形状,并获得核团中心位置、体积等数据.结论 三维重建方法对脑内神经团的三维重建切实有效,有助于对脑内神经核团形态位置的精确研究.

An identification method of nonlinear systems using least squares support vector machine(LS-SVM) is proposed. The constraints of inequalities in the classical SVM approach are replaced by equality-type constraints in LS-SVM. The LS-SVM solution follows directly from solving a set of linear equations instead of quadratic programming. A kind of nonlinear predictive control scheme based on the LS-SVM model is presented. Simulation results for a CSTR process show that LS-SVM can be trained fastly. The LS-SVM has...

An identification method of nonlinear systems using least squares support vector machine(LS-SVM) is proposed. The constraints of inequalities in the classical SVM approach are replaced by equality-type constraints in LS-SVM. The LS-SVM solution follows directly from solving a set of linear equations instead of quadratic programming. A kind of nonlinear predictive control scheme based on the LS-SVM model is presented. Simulation results for a CSTR process show that LS-SVM can be trained fastly. The LS-SVM has good ability of modeling nonlinear process and good generalization under small data set available. The nonlinear predictive control strategy based on LS-SVM model shows satisfactory performance.

探讨了利用最小二乘支持向量机(LS-SVM)进行非线性系统辨识的方法,LS-SVM用等式约束代替传统支持向量机中不等式约束,求解过程从解QP问题变成解一组等式方程.将得到的LS-SVM模型应用到非线性预测控制,提出了基于LS-SVM模型的非线性预测控制算法.通过CSTR过程仿真表明,最小二乘支持向量机学习速度快,在小样本情况下具有良好的非线性建模和泛化能力.基于LS-SVM的预测控制算法具有很好的控制性能.

 
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