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loss function相关的网络例句

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与 loss function 相关的网络例句 [注:此内容来源于网络,仅供参考]

At the same time, we prove that the best linear unbiased prediction of a linear predictable variable Qy and the best linear unbiased prediction βn=(X"sV-1Xs)-1 X"sVs-1ys of regression coefficients of the finite populations βn=(X"V-1X)-1X"V-1y are admissible under matrix loss function and in the class of the linear predictions and the class that contains all predictions, and under quadratic loss function in the class of the linear predictions.

在线性预测类和一切预测类中是可容许预测,而在二次损失函数下其在线性预测类中是可容许预测。

In this paper, the formula of Expect Bayes estimation of the reliability under entropy loss function and square loss function for geometric distribution have been given, when the prior distribution of the reliability is power distribution.

本文在几何分布的先验分布为幂分布时研究了其在熵损失函数下,可靠度的多层Bayes估计及其容许性,给出了可靠度的多层Bayes估计的计算公式。

The example are also given to show that the two Expect Bayes estimation formula are both steady and the precision of Expect Bayes estimation of the reliability for geometric distribution under the entropy loss function is higher than that of the square loss function.

通过实例验证,在熵损失函数下计算出的几何分布可靠度的多层Bayes估计是稳健的,并进一步表明在熵损失函数下计算出的几何分布可靠度的多层Bayes估计值比其在平方损失函数下算出的结果精度更高。

Finishing calculation of mean value, standard deviation, skewness, kurtosis of Beta distribution.(2) Fitting parameters of many kinds of typical distribution and using residual deviation to evaluate fitting precision.(3) Using Beta distribution as an agreed indication distribution applied to many kinds of practical photoelectric measurement distributions.(4) Deriving theory formula of Bayes point estimation about Beta distribution parameters and mean value and standard deviation on the condition of mean square error loss function and supposed the prior distribution is uniform distribution.(5) Generating MCMC sample from post distribution by the method of Gibbs sample algorithm. Calculating bayes point estimation from sample on the condition of mean square error loss function. Calculating confidence interval by an approximate method to complete interval estimation.

本文的主要工作有:(1)解决了Beta分布参数a和b的精确计算以及均值、标准差、偏度、峰度的计算问题;(2)拟合出10余种典型分布的Beta分布的两个参数,并且采用剩余标准差评价该Beta分布的拟合精度;(3)对多种典型的光学与光电测量系统的测量分布进行了Beta分布统示表示;(4)在假设先验分布为均匀分布前提下,得到参数a和b以及均值μ和标准差σ在均方误差损失函数下的贝叶斯点估计理论计算公式;(5)利用直接抽样的Gibbs抽样算法,从后验分布中产生MCMC样本,从样本直接计算均方误差损失函数下的贝叶斯点估计,并使用一种近似方法计算其置信区间,完成区间估计。

This paper discusses the defect of quadratic loss function and studies the models of inverted Normal and inverted Gamma loss function.

本文讨论了二次损失函数模型的局限,在此基础上研究了逆正态、逆伽玛损失函数模型;探讨了质量损失函数与信噪比的关系,提出了基于信噪比的多元质量损失函数模型,克服了单质量属性不能反映实际中多维质量属性的质量损失的弊端,实例证明该方法是可行的。

R-loss function is a more general form of both quadratic loss function and Laplacian loss function.

推导出了作为仿真的依据的r-SVR的解的形式和对其进行求解的牛顿迭代公式。

Finally, Bayesian methods for sample size calculation under 0~1 loss function and quadratic loss function were considered.

最后考虑了0-1损失函数和平方损失函数下计算样本容量的Bayes方法。

In the model, the non-sensitive loss function was replaced by quadratic loss function and the inequality constraints were replaced by equality constraints.

在该模型中,用二次损失函数取代支持向量机中的不敏感损失函数,将不等式约束条件变为等式约束,从而将二次规划问题转换为线性方程组的求解,并提出对核函数的盯参数进行动态选取。

The non-sensitive loss function is replaced by quadratic loss function and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups.

用二次损失函数取代支持向量机中的不敏感损失函数,将不等式约束条件变为等式约束,从而将二次规划问题转变为线性方程组的求解。

Based on the above-mentioned parameter, according to the relationship between robust estimation and nonlinear diffusion, the Turkey loss function is introduced as the diffusion function in SAR image decomposition because of its better performance, and the contour of an image is extracted by the Turkey diffusion function with the proposed diffusion parameter mentioned above. The experiment results indicate that the Turkey loss function based diffusion process can strengthen the conspicuous contour. Additionally, a Raita\'s criterion-based method, solving the automatic diffusion threshold, is proposed to automatically set the threshold in diffusion decomposition.

针对基于梯度参数和局域方差系数的扩散在对SAR图像进行分解时存在的缺陷,提出了一种局域方差系数与窗口幅度均值积的扩散系数,该系数能够更有效地实现潜在目标区域和背景区域的区分;在此基础上,根据鲁棒估计与非线性扩散的联系,引入扩散效果更好的Turkey损失函数作为图像分解中的扩散函数,并结合局域方差系数与窗口幅度均值积的扩散参数来提取图像的轮廓,结果表明:基于Turkey损失函数的扩散过程对特征突出的边缘所起的&强化作用&更加明显;此外,针对扩散分解中的阈值确定问题,提出了一种基于拉依达准则的扩散阈值自动求解方法,实现了扩散阈值的自动求解。

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