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Aug, 2012
非参数稀疏性和正则化
Nonparametric sparsity and regularization
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Lorenzo Rosasco, Silvia Villa, Sofia Mosci, Matteo Santoro, Alessandro verri
TL;DR
研究非线性模型下的监督学习与变量选择问题,提出一种基于偏导数的非参数稀疏模型,利用再生核希尔伯特空间的概念和近端方法得出最小化问题及迭代求解算法,并通过理论和实验分析表明其具有优秀的性能表现。
Abstract
In this work we are interested in the problems of
supervised learning
and
variable selection
when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to cons
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