Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7514
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dc.contributor.authorLuan, Xin-Zeen_US
dc.contributor.authorLiang, Yongen_US
dc.contributor.authorLiu, Chengen_US
dc.contributor.authorXu, Zong-Benen_US
dc.contributor.authorZhang, Haien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorChan, Tak-Mingen_US
dc.date.accessioned2023-03-17T03:23:10Z-
dc.date.available2023-03-17T03:23:10Z-
dc.date.issued2012-
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7332 LNCS(PART 2), pp. 414-421en_US
dc.identifier.isbn978-364231019-5-
dc.identifier.issn16113349-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7514-
dc.description.abstractWe investigate a net regularization method for variable selection in the linear model, which has convex loss function and concave penalty. Meanwhile, the net regularization based on the use of the Lr penalty with 1/2 ≤ r ≤ 1. In the simulation we will demonstrate that the net regularization is more efficient and more accurate for variable selection than Lasso. © 2012 Springer-Verlag.en_US
dc.language.isoenen_US
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.titleRegularization path for linear model via net methoden_US
dc.typeConference Paperen_US
dc.identifier.doi10.1007/978-3-642-31020-1_49-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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