摘要翻译：
我们提出了一类稀疏广义线性模型，其中包括probit和logistic回归作为特例，并提供了一些额外的灵活性。我们提供了一个EM算法，用于从数据中学习这些模型的参数。我们将我们的方法应用于文本分类和模拟数据中，结果表明我们的方法在总体上比logistic模型和probit模型以及弹性网络有很大的优势。
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英文标题：
《A flexible Bayesian generalized linear model for dichotomous response
  data with an application to text categorization》
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作者：
Susana Eyheramendy, David Madigan
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  We present a class of sparse generalized linear models that include probit and logistic regression as special cases and offer some extra flexibility. We provide an EM algorithm for learning the parameters of these models from data. We apply our method in text classification and in simulated data and show that our method outperforms the logistic and probit models and also the elastic net, in general by a substantial margin.
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PDF链接：
https://arxiv.org/pdf/708.0959