The randomly weighted bootstrap method provides a way of assessing the distribution of the M-estimators without estimating the nuisance quantities of the error distributions.
利用随机加权方法可以避免先对误差分布中的冗余参数进行估计。
By means of the density function fitting method of weighted sum of normal kernels the optimizing problem of template matching for non-normal distribution image is solved.
并用正态核加权和密度函数拟合法解决了非正态图象模板匹配的优化问题。
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