论文标题
通过稀疏广义添加剂模型进行分类
Classification by sparse generalized additive models
论文作者
论文摘要
我们考虑（非参数）稀疏（广义）添加剂模型（垃圾邮件）进行分类。垃圾邮件分类器的设计是基于最大程度地减少对单变量成分在正顺序序列中的扩展系数扩展（例如傅立叶或范围）的系数的稀疏组/坡度惩罚。所得的分类器本质地适应未知的稀疏性和光滑度。我们表明，在某些稀疏组限制了特征值条件下，它几乎是在整个分析，Sobolev和BESOV类的范围内同时（最多到对数因子）。在模拟和真实数据示例中说明了提出的分类器的性能。
We consider (nonparametric) sparse (generalized) additive models (SpAM) for classification. The design of a SpAM classifier is based on minimizing the logistic loss with a sparse group Lasso/Slope-type penalties on the coefficients of univariate additive components' expansions in orthonormal series (e.g., Fourier or wavelets). The resulting classifier is inherently adaptive to the unknown sparsity and smoothness. We show that under certain sparse group restricted eigenvalue condition it is nearly-minimax (up to log-factors) simultaneously across the entire range of analytic, Sobolev and Besov classes. The performance of the proposed classifier is illustrated on a simulated and a real-data examples.
