胡锡进加盟大摩 估量把老胡吓够呛 机器翻译就是这么不靠谱 担任中国区副董事长 储殷 (胡锡进干啥的)

今天上午，网络有传言称，摩根士丹利已任命胡锡进为中国区副董事长。信息称，摩根士丹利正寻求在中国拓展其投资银行业务。

今天头条认证为著名国际疑问专家的账号储殷表示，这其实是翻译软件的词库疑问。James Hu被摩根斯坦利任命为副总，James Hu不是胡锡进。

“显然是资讯的中译机翻出了缺点，机器或许更熟习‘胡锡进’的名字，所以张冠李戴了。”储殷称，机器翻译就是这么不靠谱，估量把老胡吓够呛。

小段翻译，专业的来~

尽力翻译了，希望有协助吧！==================================================================------------------------------------------------------------------With α in hand, we can attenuate the contribution of a particular kernel function to the signal expansion by weighting the corresponding coefficient α_(i,j) by a number 0≤h_(i,j)≤1. That is, we modify α_(i,j) component-wise according to_(α_(i,j))^^=h_(i,j) α_(i,j) in the sense of minimizing the mean square error. Clearly, the crucial issue is the design of the filter h. Setting h_(i,j)=0 completely removes the contribution of kernel function and setting h_(i,j)=1 leaves it unaltered. Choosing 0<h_(i,j)<1 attenuates the contribution of corresponding kernel function. Without loss of generality, let S be a small window of a fixed size (2R+1)×(2R+1) and the mean of noise to be zero, the resulting local wiener estimator is given by a scalar processor of the σ2, μ are the local variances and local means in the moving window S, respectively. It can be calculated as follows:Finally, we invert the modified Lagrange multipliers to obtain the signal estimate using Eq.(4):To verify the proposed algorithm, we present two series of experiments that compare the performance of the proposed approach with that of local Wiener filter in spatial domain. In these experiments, the radial basis function(RBF) is used as the kernel function of least support squares vector regression. For RBF kernel one Denoising Using LocalAdaptive Least Squares Support Vector squares support vector theory and results and discussion[1]Mihcak M K, Kozintsev 1, Ramchandran K(1999)Spatially adaptive statistical ,modeling of wavelet image coefficients and its application to denoising [C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Phoenix[2]Suykens J A K, Vandewalle J (1999) Least squares support vector machine classifiers [J]. Neural Processing Letters, 9(3):293-300[3]Vapnik V (1995) The nature of statistical learning theory [M]. New York:Springer- Verlag[4]Cheng Hui, Tian Jinwen, Liu Jian, et al.(2004) Wavelet domain image denoising via support vector regression [J]. Electron Len, 40(23):| 479-| 481 ----------------------------------------------------------------翻译如下----------------------------------------------------------------有了α后，我们可以经过用一个0≤h_(i,j)≤1的数给对应系数α_(i,j)加权， 来削弱一个特定的核函数对信号扩张的奉献。 也就是说，我们依据在最小化均方误差下_(α_(i,j))^^=h_(i,j) 来调整α_(i,j)的重量方式。 显然，一个关键的疑问是滤波器h的设计。 设定h_(i,j)=0会把核函数的奉献全部移除，而设定h_(i,j)=1会毫无变化。 选取0<h_(i,j)<1，来削弱对应核函数的奉献。 在不损失普遍性的前提下，设S为一个固定大小(2R+1)×(2R+1)的小窗口，设噪音的均值为0，其失掉的部分维纳估量由波形标量处置机给出。 这里σ2, μ区分是运动窗口S的部分方差和部分均值。 它的计算如下：最后，我们反置修正的拉格朗日乘数用等式4来失掉信号估值。 为了证明所提的算法，我们会出现两个用来比拟所提算法和空间域部分维纳滤波法的性能的实验系列。 在这些实验里，径向基函数（RBF）被用作最小二乘向量回归的核函数。 作为RBF核，我们可以失掉：1.用部分顺应最小二乘支持向量回归的图像减噪2.最小二乘支持向量回归3.所提出的通常和算法4.实验结果和讨论[1] Mihcak M K, Kozintsev 1, Ramchandran K(1999)空间可适统计，小波图像系数建模以及它在图像降噪上的运行[C]. IEEE国际声学、语音学及信号处置讨论会，菲尼克斯[2] Suykens J A K, Vandewalle J (1999)最小二乘支持向量机分级器[J]. 神经处置通讯9(3):293-300[3] Vapnik V (1995) 统计学习通常的实质[M].纽约：施普林格出版公司[4] Cheng Hui, Tian Jinwen, Liu Jian等.(2004)经由支持向量回归的小波域图像[J].莱茵电子, 40(23):| 479-| 481-----------------------------------------------------------------=================================================================以上

高分求英文翻译！！

看来是升职开放，祝你好运 salaryRMB(after tax,bonus included) expected salaryRMB(after tax)2.2008-2010,half month off work of each year to attend the CFO advanced close training，and obtained the -evaluation:willing to take responsibility,hard working and dependable，good communicate and coordinate team cooperating position:chief financial director,least initiate time:in one month

学编程是不是有学好几种言语啊，学一种不就行了吗？

先学一种经典的，等你会了，精了后，再触及其他的就十分容易了，关键是要掌握编程思想，刚末尾切忌今天学这个，明天学那个，先学好一个才是正派。 学会了就好办了。