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training samples
相关语句
  训练样本
     Denote by θ_(kn) the nearest neighbour discriminator of θ based on the training samples (θ_1,X_1),…, (θ_n,X_n)and the observed X.
     又设ρ是x上的某一距离函数,θ_(kn)表示基于训练样本(θ_1,X_),…,(θ_n,X_n)与X观察值的θ的最近邻判别值.
短句来源
     The estimate of L_n is defined as where θ_(nj) denotes the NN prediction of θ,, based on the training samples (X_1, θ_1),. . .
     设是L_n的估计量,其中θ_(nj),是按训练样本(X_1,θ_1),…,(X_(j-1),θ_(j-1)),(X_(j+1),θ_(j+1)),…(X_n,θ_n)与观察到的X_j对θ_j所作的NN预测。
短句来源
     For training samples their correct classification rate is 100%.
     训练样本的分类正确率达100%。
短句来源
     About 70 percent of all cases (up to 103 cases) were selected randomly to form training samples set, on which BP neural networks and Logistic regression model were built.
     从145个样本中随机选择70%左右的样本(103例)作为训练样本,建立BP神经网络诊断模型和Logistic回归模型。
短句来源
     It was demonstrated that 83.33% specificity and 100% sensitivity in serum protein fingerprints clsssification and decision tree constructed by training samples.
     以28、60 d的对照组和染矽尘大鼠组为训练样本,血清蛋白指纹诊断决策树图分类敏感性和特异性分别达100%、83.33%。
短句来源
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  学习样本
     It can also be used to generate training samples for a BP neural network.
     也可以通过它产生BP神经网络的学习样本,使得利用神经网络实现动态信号配时成为可能.
短句来源
     Environmental quality standards for surface water of China(GB3838-88)acted as training samples,14 indexes including oxygen-balance parameters,nutrient elements,heavy metal ions and oils were selected,and water quality classification for the mainstream and major tributaries of the Yangtze River was studied with neural network-BP model.
     运用我国地面水环境质量标准 (GB3838-88)作为学习样本 ,选取了包括氧平衡参数、营养元素、重金属离子、油类等 14个指标 ,运用人工神经网络BP模型对长江干流和主要支流 (含湖泊 )的水环境质量进行分类研究。
短句来源
     The training samples of FNNs are the reflectivity obtained by measuring objects surface out of the Lab. The input nerve cells are the planet reflectivities, air corrected reflectivities using by 6S models, and the classified types on the spectral character.
     选择实测典型地物中反射率随时间变化较小的地物类型,以其反射率值作为网络的学习样本,对网络进行训练,然后将行星反射率、6S大气校正后的反射率以及按光谱特征分类数据输入到训练后的网络进行模拟,仿真输出地面反射率。
短句来源
     (3) Considering the training samples are unbalanced among different target categories, and the false accept rate of recognition system using classical classifier is high, general algorithms of one-class classifier are discussed.
     首次将单类分类算法引入到地面目标声信号识别的研究中,并提出了两种新的单类分类算法,以解决现有识别算法存在的系统误识别率高、学习样本数目不均衡情况下性能恶化的难题。
短句来源
     Based on analyzing the vertical response characteristic of conventional sonic logging, researching thoroughly the very complex nonlinear relationship between conventional sonic log with low resolution and slowness log with high resolution, forecast model is established using training samples consisting of key wells' slowness curves with high, low vertical resolution to create the other well's slowness curve with higher vertical resolution by means of Artificial Neural Networks(ANN) technique.
     本文在分析了常规声波测井的纵向响应特征,深入研究了低分辨率声波时差曲线和高分辨率声波时差曲线之间的非常复杂的非线性映射关系的基础上,将关键井高、低分辨率的声波时差曲线作为学习样本,构建学习样本集,以人工神经网络为技术手段,建立反演预测模型,进而生成其它井的高分辨率声波时差曲线。
短句来源
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  “training samples”译为未确定词的双语例句
     Based on the errors of training samples,the topology of 5∶20∶1 is the best one.
     通过比较,最优的网络拓朴结构为5∶20∶1。
短句来源
     The final model was derived from 10 randomly chosen training samples and the prognostic validity of the China Integrated Staging score (CIS score) was assessed on the corresponding testing samples.
     测试组评估新分期(the China Integrated Staging score,CIS score)的效度。
短句来源
     The network structure with 3 4 1 is established and some data are collected to form training samples of the network.
     建立了一个3-4-1的网络结构;
短句来源
     140 cases were randomly selected to form the training samples,on which artificial neural networks model(BP networks) was built and compared with Logistic model from Statistical Package for the Social Science(SPSS) software.
     采用完全随机法从中选择140例样本作为训练集,建立人工神经网络(BP网络)诊断模型,并与软件SPSS分析处理的Logistic回归模型作比较。
短句来源
     RESULT: In superoxide analysis,the average simulated deviations of training samples ASEr(between the simulated data and the experimented values)≤0.150,the predicted deviation of predicting samples PEr≤ 0.181.In hydrogen peroxide analysis,ASEr≤0.110,PEr≤0.191.CONCLUSION: The network was stably established by PCA-ANN technique.
     结果:清除超氧阴离子训练集的平均拟合偏差ASEr(拟合值与实验值之间的相对偏差)≤0.150,预测集的预报偏差PEr≤0.181; 清除过氧化氢的ASEr≤0.110,PEr≤0.191。
短句来源
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  training samples
-?We consider the problem of multivariate outlier testing for purposes of distinguishing seismic signals of underground nuclear events from training samples based on non-nuclear seismic events when certain data are missing.
      
The formulation allows for the training samples used in the design to have correlations in time, and for use of a wide range of neural network probability models including recurrent structures.
      
Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters.
      
The insufficiency of training samples both in quantities and varieties constrains the generalization ability of the classifier significantly.
      
In practice, the number of training samples is very small because the users are often impatient.
      
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Let(θ,X), (θ_1,X_1),…, (θ_n,X_n) be lid random Vectors, θ∈{0,1}, X∈x{0,1,2,…training samples (θ_1,X_1),…, (θ_n,X_n)and the observed X.Put Tn=P(θ_(kn)≠θ|X_1,…X_n), L_n=P(θ_(kn)≠θ|θ_1,…,θ_n ,X_1,…,X_n), R=E(P(θ=0|X)P(θ=1|X)]. This note gives a sufficient and necessary condition for L+_n→R (a.s.) as n→∞. Also, it is shown that T_n→R (a.s.) as n→∞. Finally, a strong consistent estimate of R is...

Let(θ,X), (θ_1,X_1),…, (θ_n,X_n) be lid random Vectors, θ∈{0,1}, X∈x{0,1,2,…training samples (θ_1,X_1),…, (θ_n,X_n)and the observed X.Put Tn=P(θ_(kn)≠θ|X_1,…X_n), L_n=P(θ_(kn)≠θ|θ_1,…,θ_n ,X_1,…,X_n), R=E(P(θ=0|X)P(θ=1|X)]. This note gives a sufficient and necessary condition for L+_n→R (a.s.) as n→∞. Also, it is shown that T_n→R (a.s.) as n→∞. Finally, a strong consistent estimate of R is given.

设(θ,X),(θ_1,X,),…,(θ_n,X_n)是独立同分布的随机向量,θ∈{0,1},X∈x{0,1,2,…

The physiological and chemical basis of Di-Region theory proposedby one of the authors is investigated in this paper, and some fundame-tal ideas related to the establishment of Di-Region theory areintroduced. Based on Di-Region theoy, we made a tentative research on the discriminating classification of the carcinogenic activities of polycyclic aromatic hydrocarbons (PAH) by the pattern recognition method centred on quantification theory. The results obtained from the discriminating classification of training...

The physiological and chemical basis of Di-Region theory proposedby one of the authors is investigated in this paper, and some fundame-tal ideas related to the establishment of Di-Region theory areintroduced. Based on Di-Region theoy, we made a tentative research on the discriminating classification of the carcinogenic activities of polycyclic aromatic hydrocarbons (PAH) by the pattern recognition method centred on quantification theory. The results obtained from the discriminating classification of training samples are very close to that of animal experiments. Prediction is made for the carcinogenic activities of some PAH which have not been tested with animals. The consistency of the results of the pattern recognition method and the routine calculation by the di-regional equation provides statistically an evidence for the rationality of the di-regional theory.

本文探讨了多环芳烃致癌性能双区理论的生理和化学基础,介绍了作者之一提出双区理论时的某些依据。并以此为基础,用以数量化方法为中心的模式辨认方法对多环芳烃致癌性能的判别分类作了初步研究。在对训练样本的判别分类中获得了和动物实验十分接近的结果,对若干未作动物实验的多环芳烃化合物进行了致癌性能的预测。模式辨认结果和双区公式常规计算结果的相互一致,从统计上对双区理论的合理性提供了依据。

Let (X,θ) be a random vector, X∈R~4,θ∈R~1, and (X_i, θ,)be i. i. d. random samples of (X,θ),i=1, ..., n, and let L_n be the conditional risk in the nearest neighbor (NN) prediction under square loss. The estimate of L_n is defined as where θ_(nj) denotes the NN prediction of θ,, based on the training samples (X_1, θ_1),...,(X_(?),θ_(?)),(X_((?)+1),θ_((?)+1)),..., (X_n,θ_n) and the observed X_i. It is well-known that L_n→2R, a. s. under certain conditions, where R is the Bayesian risk. This articla has...

Let (X,θ) be a random vector, X∈R~4,θ∈R~1, and (X_i, θ,)be i. i. d. random samples of (X,θ),i=1, ..., n, and let L_n be the conditional risk in the nearest neighbor (NN) prediction under square loss. The estimate of L_n is defined as where θ_(nj) denotes the NN prediction of θ,, based on the training samples (X_1, θ_1),...,(X_(?),θ_(?)),(X_((?)+1),θ_((?)+1)),..., (X_n,θ_n) and the observed X_i. It is well-known that L_n→2R, a. s. under certain conditions, where R is the Bayesian risk. This articla has gotten the complete convergence rates of L_n, that is, for every ε>0, under E|θ|~(2+δ)<∞(δ>0) and other conditions.

设(X,θ)是随机向量,X∈R~d、θ∈R~1;(X_i,θ_i)是(X,θ)的i.i.d.随机样本,i=1,…,(?)bjL_n是平方损失下最近邻(NN)预测的条件风险.设是L_n的估计量,其中θ_(nj),是按训练样本(X_1,θ_1),…,(X_(j-1),θ_(j-1)),(X_(j+1),θ_(j+1)),…(X_n,θ_n)与观察到的X_j对θ_j所作的NN预测。众所周知,在一定的条件下,L_n→2R~*,α,s.,其中R~*是Bayes风险。本文得到了L_n的完全收敛速度,即在E|θ|~(2+δ)<∞(δ>0)及其它条件下证明了

 
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