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Dual form back propagation on the EM algorithm

机译:EM算法的双重形式反向传播

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Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functions, in stead, a risk formula based on the least square method is used. The gradient descending approach should be used in such kind approaches. Such kind EM algorithms can estimate the parameters of a random model from both labeled and unlabeled samples, and are suitable for semi-supervised learning.
机译:弗拉基米尔·瓦普尼克(Vladimir N.Vapnik)。 (1998年)指出,EM算法中的极大似然函数只是一种特殊的风险函数。基于此观点,一种新颖的EM算法使用的风险函数与最大似然函数不同,而是使用基于最小二乘法的风险公式。梯度下降法应在此类方法中使用。这类EM算法可以从标记和未标记的样本中估计随机模型的参数,并且适合于半监督学习。

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