英语翻译AbstractAlogisticregressionclassificationalgorithmisdevelopedforproblemsinwhichthefeaturevectorsmaybemissingdata(features).Singleormultipleimputationforthemissingdataisavoidedbyperforminganalyticintegra

Question

英语翻译
Abstract
A logistic regression classification algorithm is developed for problems in which the feature vectors may be missing data(features).
Single or multiple imputation for the missing data is avoided by performing analytic integration with an estimated conditional
density function (conditioned on the nonmissing data).Conditional density functions are estimated using a Gaussian mixture model (GMM),with parameter estimation performed using both expectation maximization (EM) and Variational Bayesian EM(VB-EM).Using widely available real data,we demonstrate the general advantage of the
VB-EM GMM estimation for handling incomplete data,vis-`a-vis the EM algorithm.
Moreover,it is demonstrated that the approach proposed here is generally superior to standard imputation procedures.

Answer 1

摘要
A后勤退化classification算法为特点传染媒介也许是缺掉数据的问题被开发(特点).
Single或缺掉数据的多归咎通过进行分析综合化避免与一估计的有条件
density作用(适应在nonmissing的数据).使用一个高斯混合物模型(GMM),条件密度函数估计,当参量估计进行使用期望最大化(EM)和变化贝叶斯EM (VB-EM).使用广泛可用的真正的数据,我们展示一般好处的 处理的残缺不全的数据,力`力VB-EM GMM估计EM算法.
Moreover,被展示提出的方法这里在标准归咎做法通常是优越