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Research on Neural Computation Described by Scientists at New York University, Courant Institute of Mathematical Sciences

机译:纽约大学,科兰特数学科学研究所科学家描述的神经计算研究

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2011 FEB 14 - (VerticalNews.com) -- Data detailed in 'Least squares estimationrnwithout priors or supervision' have been presented. "Selection of an optimal estimator typicallyrnrelies on either supervised training samples (pairs of measurements and their associated truernvalues) or a prior probability model for the true values. Here, we consider the problem ofrnobtaining a least squares estimator given a measurement process with known statistics (i.e., arnlikelihood function) and a set of unsupervised measurements, each arising from a correspondingrntrue value drawn randomly from an unknown distribution," scientists writing in the journalrnNeural Computation report.
机译:2011年2月14日-(VerticalNews.com)-已提供“没有先验或监督的最小二乘估计”中详细的数据。 “最佳估计量的选择通常取决于监督训练样本(成对的测量值及其相关的真实值)或真实值的先验概率模型。在此,我们考虑在给定具有已知统计数据的测量过程的情况下获得最小二乘估计量的问题( ”,即“似然函数”)和一组无监督的测量值,每个测量值均来自从未知分布中随机抽取的相应真实值。”科学家在《神经计算》杂志上写道。

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