A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
Kernel methods provide powerful and flexible tools for nonlinear learning in high dimensional data analysis, but feature selection remains a challenge in kernel learning. The proposed DOSK method provides a new unified framework to implement kernel methods while automatically selecting important variables and identifying a subset of parsimonious knots at the same time. A double penalty is employed to encourage sparsity in both feature weights and representer coefficients. The authors havedoi:10.4310/sii.2018.v11.n3.a4 fatcat:lfvqz7nmqnb27mgyhy2tpl5oa4