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absolute inequality相关的网络例句

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

Chebyshev's Inequality and Law of Large Numbers are the foundational work on the limit theory of probability.

其创立的切贝舍夫不等式和切贝舍夫大数定律是概率论极限理论的基础,从而使概率论成为严密数学分支。

While Chebyshev's Inequality gives an upper bound on P, there is no guarantee that the upper bound is a good approximation.

因为切尔雪夫不等式给出的是每个象素灰度的上限,而又没法保证其上限是一个很好的估计。

We then apply the one-tailed version of Chebyshev's Inequality to estimate the percentage of light reaching a surface at depth t.

这样我们应用一个修改版的切尔雪夫不等式去估计光源到达深度t平面的百分比。

In addition, anomaly detection can be implemented by Chebyshev inequality in raw alert series, filtered alert series, frequency series of alerts. Then the capability of detecting specific security events can be improved.

另外还采用切比雪夫公式对告警时间序列、误告警去除后的时间序列、时频转换后的频域序列进行异常检测,提高了检测特定类型安全事件的能力。

Aiming at the problems of over-fitting and generalization for neural networks, the locally most entropic colored Gaussian joint input-output probability density function estimate is studied, and a new method by means of Chebyshev inequality is proposed to self-revise respectively according to every cluster.

针对神经网络的过拟合和泛化能力差的问题,研究了样本数据的输入输出混合概率密度函数的局部最大熵密度估计,提出了运用Chebyshev不等式的样本参数按类分批自校正方法,以此估计拉伸样本集,得到新的随机扩充训练集。

And then , the author analyzes its complete and necessary condition for foundation of Chebyshev's Ineuquality. Furthermore, the paper makes a demonstration again for Chebyshev's Inequality with the method of modern probability.

最后给出了切比雪夫不等式其等号成立的充要条件,并用现代概率方法重新证明了切比雪夫不等式。

The visibility function over an arbitrary filter region can be estimated using Chebyshev's Inequality, which yields an upper bound on the amount of light reaching the fragment being shaded.

在一个任意的过滤区域的可见性函数可以通过切尔雪夫不等式来估计,此不等式产生一个所有光照达到此着色像素总和的最高限。

We can thus choose any monotonic warp of the depth metric and still obtain an upper bound on the visibility using Chebyshev's Inequality as with standard VSMs.

我们可以选择一个单调的深度扭曲矩阵,但还是能够得到运用切尔雪夫不等式得到可见度的最高限。

I. d. Bernoulli random variables. First, we can get the upper bound with order from Chebyshev inequality. Second, a better upper bound was gotten with the new inequalities, which can fasten the convergence rate.

并对服从两点分布的独立随机变量X1,X2,…,X进行研究,首先利用Chebyshev不等式得到随机变量偏离方差ES的上界,但它只有阶的收敛速度,然后利用新的不等式得到一个新的上界,它有更快的收敛速度,这在探讨收敛速度时有着重要的理论意义。

Moreover, with its help people can also evaluate the fault-tolerant performance of ANN. In this paper, Chebyshev Inequality method, as a kind of evaluating thought, was firstly concerned in the faulttolerance analysis of ANN; 4. The variation of faulty behaviors of ANNs during the course of learning was studied by taking the discrete Hopfield neural network and Hebb learning algorithm as examples.

这种方法首次将切比雪夫不等式引入人工神经网络容错性分析当中,从而提出了对人工神经网络容错性分析采用估算的思想; 4 以离散型Hopfield神经网络和Hebb学习算法为例,着重研究了在学习过程中人工神经网络故障性能的变化。

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